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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9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%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAA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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI2OyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjV0NTcgLTIxNC41IHQxNTUuNSAtMTU1LjV0MjE0LjUgLTU3dDIxNC41IDU3dDE1NS41IDE1NS41dDU3IDIxNC41dC01NyAyMTQuNXQtMTU1LjUgMTU1LjV0LTIxNC41IDU3ek01MjUgOTAwaDE1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTI3NWgxMzdxMjEgMCAyNiAtMTEuNXQtOCAtMjcuNWwtMjIzIC0yNzVxLTEzIC0xNiAtMzIgLTE2dC0zMiAxNmwtMjIzIDI3NXEtMTMgMTYgLTggMjcuNXQyNiAxMS41aDEzN3YyNzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXogIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAyNzsiIGQ9Ik02MDAgMTE3N3ExMTcgMCAyMjQgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNHQtNDUuNSAtMjI0dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXpNNjAwIDEwMjdxLTExNiAwIC0yMTQuNSAtNTd0LTE1NS41IC0xNTUuNXQtNTcgLTIxNC41dDU3IC0yMTQuNSB0MTU1LjUgLTE1NS41dDIxNC41IC01N3QyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNXQtNTcgMjE0LjV0LTE1NS41IDE1NS41dC0yMTQuNSA1N3pNNjMyIDkxNGwyMjMgLTI3NXExMyAtMTYgOCAtMjcuNXQtMjYgLTExLjVoLTEzN3YtMjc1cTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2Mjc1aC0xMzdxLTIxIDAgLTI2IDExLjV0OCAyNy41bDIyMyAyNzVxMTMgMTYgMzIgMTYgdDMyIC0xNnoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDM1OyIgZD0iTTYwMCAxMTc1cTExNiAwIDIyNyAtNDkuNXQxOTIuNSAtMTMxdDEzMSAtMTkyLjV0NDkuNSAtMjI3di0zMDBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djMwMHEwIDEyNyAtNzAuNSAyMzEuNXQtMTg0LjUgMTYxLjV0LTI0NSA1N3QtMjQ1IC01N3QtMTg0LjUgLTE2MS41dC03MC41IC0yMzEuNXYtMzAwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTAgcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzAwcTAgMTE2IDQ5LjUgMjI3dDEzMSAxOTIuNXQxOTIuNSAxMzF0MjI3IDQ5LjV6TTIyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwcTAgOCA2IDE0dDE0IDZ6TTgyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwIHEwIDggNiAxNHQxNCA2eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzY7IiBkPSJNMzIxIDgxNGwyNTggMTcycTkgNiAxNSAyLjV0NiAtMTMuNXYtNzUwcTAgLTEwIC02IC0xMy41dC0xNSAyLjVsLTI1OCAxNzJxLTIxIDE0IC00NiAxNGgtMjUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoMjUwcTI1IDAgNDYgMTR6TTkwMCA2NjhsMTIwIDEyMHE3IDcgMTcgN3QxNyAtN2wzNCAtMzRxNyAtNyA3IC0xN3QtNyAtMTdsLTEyMCAtMTIwbDEyMCAtMTIwcTcgLTcgNyAtMTcgdC03IC0xN2wtMzQgLTM0cS03IC03IC0xNyAtN3QtMTcgN2wtMTIwIDExOWwtMTIwIC0xMTlxLTcgLTcgLTE3IC03dC0xNyA3bC0zNCAzNHEtNyA3IC03IDE3dDcgMTdsMTE5IDEyMGwtMTE5IDEyMHEtNyA3IC03IDE3dDcgMTdsMzQgMzRxNyA4IDE3IDh0MTcgLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNzsiIGQ9Ik0zMjEgODE0bDI1OCAxNzJxOSA2IDE1IDIuNXQ2IC0xMy41di03NTBxMCAtMTAgLTYgLTEzLjV0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNiBsLTI5IDIzcS03IDcgLTguNSAxNi41dDQuNSAxNy41cTcyIDEwMyA3MiAyMjlxMCAxMzIgLTc4IDIzOHEtNiA4IC00LjUgMTh0OS41IDE3bDI5IDIycTcgNSAxNSA1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzg7IiBkPSJNOTY3IDEwMDRoM3ExMSAtMSAxNyAtMTBxMTM1IC0xNzkgMTM1IC0zOTZxMCAtMTA1IC0zNCAtMjA2LjV0LTk4IC0xODUuNXEtNyAtOSAtMTcgLTEwaC0zcS05IDAgLTE2IDZsLTQyIDM0cS04IDYgLTkgMTZ0NSAxOHExMTEgMTUwIDExMSAzMjhxMCA5MCAtMjkuNSAxNzZ0LTg0LjUgMTU3cS02IDkgLTUgMTl0MTAgMTZsNDIgMzNxNyA1IDE1IDV6TTMyMSA4MTRsMjU4IDE3MnE5IDYgMTUgMi41dDYgLTEzLjV2LTc1MHEwIC0xMCAtNiAtMTMuNSB0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNmwtMjkgMjNxLTcgNyAtOC41IDE2LjV0NC41IDE3LjVxNzIgMTAzIDcyIDIyOXEwIDEzMiAtNzggMjM4IHEtNiA4IC00LjUgMTguNXQ5LjUgMTYuNWwyOSAyMnE3IDUgMTUgNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTAxOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek03MDQgOTAwaC0yMDhxLTIwIDAgLTMyIC0xNC41dC04IC0zNC41bDU4IC0zMDJxNCAtMjAgMjEuNSAtMzQuNSB0MzcuNSAtMTQuNWg1NHEyMCAwIDM3LjUgMTQuNXQyMS41IDM0LjVsNTggMzAycTQgMjAgLTggMzQuNXQtMzIgMTQuNXpNNjc1IDQwMGgtMTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMTUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoMTUwcTEwIDAgMTcuNSA3LjV0Ny41IDE3LjV2MTUwcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXoiIC8%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTM0OyIgZD0iTTYwMCAxMTc4cTExOCAwIDIyNSAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI1dC00NS41IC0yMjV0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNSAtNDUuNXQtMjI1IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNXQ0NS41IDIyNXQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNSA0NS41ek02NzUgOTAwaC0xNTBxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di0yNzVoLTEzN3EtMjEgMCAtMjYgLTExLjUgdDggLTI3LjVsMjIzIC0yNzVxMTMgLTE2IDMyIC0xNnQzMiAxNmwyMjMgMjc1cTEzIDE2IDggMjcuNXQtMjYgMTEuNWgtMTM3djI3NXEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNTsiIGQ9Ik02MDAgMTE3NnExMTYgMCAyMjIuNSAtNDZ0MTg0IC0xMjMuNXQxMjMuNSAtMTg0dDQ2IC0yMjIuNXQtNDYgLTIyMi41dC0xMjMuNSAtMTg0dC0xODQgLTEyMy41dC0yMjIuNSAtNDZ0LTIyMi41IDQ2dC0xODQgMTIzLjV0LTEyMy41IDE4NHQtNDYgMjIyLjV0NDYgMjIyLjV0MTIzLjUgMTg0dDE4NCAxMjMuNXQyMjIuNSA0NnpNNjI3IDExMDFxLTE1IC0xMiAtMzYuNSAtMjAuNXQtMzUuNSAtMTJ0LTQzIC04dC0zOSAtNi41IHEtMTUgLTMgLTQ1LjUgMHQtNDUuNSAtMnEtMjAgLTcgLTUxLjUgLTI2LjV0LTM0LjUgLTM0LjVxLTMgLTExIDYuNSAtMjIuNXQ4LjUgLTE4LjVxLTMgLTM0IC0yNy41IC05MXQtMjkuNSAtNzlxLTkgLTM0IDUgLTkzdDggLTg3cTAgLTkgMTcgLTQ0LjV0MTYgLTU5LjVxMTIgMCAyMyAtNXQyMy41IC0xNXQxOS41IC0xNHExNiAtOCAzMyAtMTV0NDAuNSAtMTV0MzQuNSAtMTJxMjEgLTkgNTIuNSAtMzJ0NjAgLTM4dDU3LjUgLTExIHE3IC0xNSAtMyAtMzR0LTIyLjUgLTQwdC05LjUgLTM4cTEzIC0yMSAyMyAtMzQuNXQyNy41IC0yNy41dDM2LjUgLTE4cTAgLTcgLTMuNSAtMTZ0LTMuNSAtMTR0NSAtMTdxMTA0IC0yIDIyMSAxMTJxMzAgMjkgNDYuNSA0N3QzNC41IDQ5dDIxIDYzcS0xMyA4IC0zNyA4LjV0LTM2IDcuNXEtMTUgNyAtNDkuNSAxNXQtNTEuNSAxOXEtMTggMCAtNDEgLTAuNXQtNDMgLTEuNXQtNDIgLTYuNXQtMzggLTE2LjVxLTUxIC0zNSAtNjYgLTEyIHEtNCAxIC0zLjUgMjUuNXQwLjUgMjUuNXEtNiAxMyAtMjYuNSAxNy41dC0yNC41IDYuNXExIDE1IC0wLjUgMzAuNXQtNyAyOHQtMTguNSAxMS41dC0zMSAtMjFxLTIzIC0yNSAtNDIgNHEtMTkgMjggLTggNThxNiAxNiAyMiAyMnE2IC0xIDI2IC0xLjV0MzMuNSAtNHQxOS41IC0xMy41cTcgLTEyIDE4IC0yNHQyMS41IC0yMC41dDIwIC0xNXQxNS41IC0xMC41bDUgLTNxMiAxMiA3LjUgMzAuNXQ4IDM0LjV0LTAuNSAzMnEtMyAxOCAzLjUgMjkgdDE4IDIyLjV0MTUuNSAyNC41cTYgMTQgMTAuNSAzNXQ4IDMxdDE1LjUgMjIuNXQzNCAyMi41cS02IDE4IDEwIDM2cTggMCAyNCAtMS41dDI0LjUgLTEuNXQyMCA0LjV0MjAuNSAxNS41cS0xMCAyMyAtMzEgNDIuNXQtMzcuNSAyOS41dC00OSAyN3QtNDMuNSAyM3EwIDEgMiA4dDMgMTEuNXQxLjUgMTAuNXQtMSA5LjV0LTQuNSA0LjVxMzEgLTEzIDU4LjUgLTE0LjV0MzguNSAyLjVsMTIgNXE1IDI4IC05LjUgNDZ0LTM2LjUgMjR0LTUwIDE1IHQtNDEgMjBxLTE4IC00IC0zNyAwek02MTMgOTk0cTAgLTE3IDggLTQydDE3IC00NXQ5IC0yM3EtOCAxIC0zOS41IDUuNXQtNTIuNSAxMHQtMzcgMTYuNXEzIDExIDE2IDI5LjV0MTYgMjUuNXExMCAtMTAgMTkgLTEwdDE0IDZ0MTMuNSAxNC41dDE2LjUgMTIuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTM2OyIgZD0iTTc1NiAxMTU3cTE2NCA5MiAzMDYgLTlsLTI1OSAtMTM4bDE0NSAtMjMybDI1MSAxMjZxNiAtODkgLTM0IC0xNTYuNXQtMTE3IC0xMTAuNXEtNjAgLTM0IC0xMjcgLTM5LjV0LTEyNiAxNi41bC01OTYgLTU5NnEtMTUgLTE2IC0zNi41IC0xNnQtMzYuNSAxNmwtMTExIDExMHEtMTUgMTUgLTE1IDM2LjV0MTUgMzcuNWw2MDAgNTk5cS0zNCAxMDEgNS41IDIwMS41dDEzNS41IDE1NC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMzc7IiBob3Jpei1hZHYteD0iMTIyMCIgZD0iTTEwMCAxMTk2aDEwMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMHEwIC00MSAtMjkuNSAtNzAuNXQtNzAuNSAtMjkuNWgtMTAwMHEtNDEgMCAtNzAuNSAyOS41dC0yOS41IDcwLjV2MTAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXpNMTEwMCAxMDk2aC0yMDB2LTEwMGgyMDB2MTAwek0xMDAgNzk2aDEwMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMHEwIC00MSAtMjkuNSAtNzAuNXQtNzAuNSAtMjkuNWgtMTAwMCBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41djEwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjV6TTExMDAgNjk2aC01MDB2LTEwMGg1MDB2MTAwek0xMDAgMzk2aDEwMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMHEwIC00MSAtMjkuNSAtNzAuNXQtNzAuNSAtMjkuNWgtMTAwMHEtNDEgMCAtNzAuNSAyOS41dC0yOS41IDcwLjV2MTAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXpNMTEwMCAyOTZoLTMwMHYtMTAwaDMwMHYxMDB6ICIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMzg7IiBkPSJNMTUwIDEyMDBoOTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41dC0xNC41IC0zNS41dC0zNS41IC0xNC41aC05MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6TTcwMCA1MDB2LTMwMGwtMjAwIC0yMDB2NTAwbC0zNTAgNTAwaDkwMHoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjE5OyIgZD0iTTIwMCAxMTAwaDgwMHE4MyAwIDE0MS41IC01OC41dDU4LjUgLTE0MS41di0yMDBoLTEwMHEwIDQxIC0yOS41IDcwLjV0LTcwLjUgMjkuNWgtMjUwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41aC0xMDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjVoLTI1MHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNWgtMTAwdjIwMHEwIDgzIDU4LjUgMTQxLjV0MTQxLjUgNTguNXpNMTAwIDYwMGgxMDAwcTQxIDAgNzAuNSAtMjkuNSB0MjkuNSAtNzAuNXYtMzAwaC0xMjAwdjMwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjV6TTMwMCAxMDB2LTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djUwaDIwMHpNMTEwMCAxMDB2LTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djUwaDIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yM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<script src="data:application/x-javascript;base64,/*!
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<h1 class="title toc-ignore">BDD_WD2018_replication_SI.R</h1>
<h4 class="author"><em>mbuntaine</em></h4>
<h4 class="date"><em>Thu Oct 4 15:24:38 2018</em></h4>

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<pre class="r"><code>######################################################################################
######################################################################################
#Replication code for:

#Mark T. Buntaine, Brigham Daniels, Colleen Devlin
#Can information outreach increase participation in community-driven development? A field experiment near Bwindi National Park, Uganda
#World Development, 106: 407-421

#Prepared by: Mark Buntaine
#Mark Buntaine contact (as of September 2018): buntaine@bren.ucsb.edu

#Compiled using R Version 3.4.3 (version &quot;Joy in Playing&quot;) on Mac running OS X 10.13.6</code></pre>
<pre class="r"><code>#Note: all analysis blocks require setup through line 374 run first
######################################################################################
######################################################################################

root &lt;- &quot;~/Google Drive/Bwindi project/Code/WD_Replication&quot;
#Note: all code and data files should be placed in a single directory
#Note: change the file path &quot;root&quot; to the local working directory
setwd(root)

######################################################################################
###Packages
######################################################################################
library(interplot) #Version 0.1.5</code></pre>
<pre><code>## Loading required package: ggplot2</code></pre>
<pre><code>## Loading required package: abind</code></pre>
<pre><code>## Loading required package: arm</code></pre>
<pre><code>## Loading required package: MASS</code></pre>
<pre><code>## Loading required package: Matrix</code></pre>
<pre><code>## Loading required package: lme4</code></pre>
<pre><code>## Warning: package 'lme4' was built under R version 3.4.4</code></pre>
<pre><code>## 
## arm (Version 1.9-3, built: 2016-11-21)</code></pre>
<pre><code>## Working directory is /Users/mbuntaine/Google Drive/Bwindi project/Code/WD_Replication</code></pre>
<pre class="r"><code>library(interflex) #Version 1.0.3
library(lfe) #Version 2.6-2291
library(Lmoments) #Version 1.2-3, for inter.binning.90 function


######################################################################################
###Functions
######################################################################################

mean.imp &lt;- function(data,var,cluster){
  for (i in 1:length(unique(data[,cluster]))){
    imp.value &lt;- mean(data[data[,cluster]==unique(data[,cluster])[i], var], na.rm=T)
    data[data[,cluster]==unique(data[,cluster])[i] &amp; is.na(data[,var]), var] &lt;- imp.value
  }
  return(data)
} #This function automates mean imputation by cluster


felm.ri &lt;- function(formula, dta, treat.var, sims, ...){
  require(lfe)
  
  ate &lt;- coef(felm(formula, data=dta))[1]
  N &lt;- felm(formula, data=dta)$N
  ate.samp.dist &lt;- rep(NA,sims)
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- dta[,ncol(M)+i]
    ate.samp.dist[i] &lt;- coef(felm(formula, data=dta))[1]
  }
  
  p.two.way &lt;- sum(abs(ate)&lt;abs(ate.samp.dist))/sims
  p.one.way.greater &lt;- sum(ate&lt;ate.samp.dist)/sims
  p.one.way.lesser &lt;- sum(ate&gt;ate.samp.dist)/sims
  se &lt;- sd(ate.samp.dist)
  
  coun &lt;- list(&quot;ate&quot; = ate, &quot;ate.samp.dist&quot; = ate.samp.dist, &quot;se&quot;=se, &quot;p.two.way&quot; = p.two.way, &quot;p.one.way.greater&quot; = p.one.way.greater, &quot;p.one.way.lesser&quot; = p.one.way.lesser, &quot;N&quot; = N)
  return(coun)
}


lm.ri &lt;- function(formula, dta, treat.var, sims, ...){
  require(lfe)
  
  ate &lt;- coef(lm(formula, data=dta))[2]
  N &lt;- nobs(lm(formula, data=dta))
  ate.samp.dist &lt;- rep(NA,sims)
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- dta[,ncol(M)+i]
    ate.samp.dist[i] &lt;- coef(lm(formula, data=dta))[2]
  }
  
  p.two.way &lt;- sum(abs(ate)&lt;abs(ate.samp.dist))/sims
  p.one.way.greater &lt;- sum(ate&lt;ate.samp.dist)/sims
  p.one.way.lesser &lt;- sum(ate&gt;ate.samp.dist)/sims
  se &lt;- sd(ate.samp.dist)
  
  coun &lt;- list(&quot;ate&quot; = ate, &quot;ate.samp.dist&quot; = ate.samp.dist, &quot;se&quot;=se, &quot;p.two.way&quot; = p.two.way, &quot;p.one.way.greater&quot; = p.one.way.greater, &quot;p.one.way.lesser&quot; = p.one.way.lesser, &quot;N&quot; = N)
  return(coun)
}

felm.ri.power &lt;- function(formula, dta, treat.var, sims, ate.bar, outcome.var, te, ...){
  require(lfe)
  
  ate.samp.dist &lt;- rep(NA,sims)
  outcome.store &lt;- dta[,outcome.var]
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- dta[,ncol(M)+i]
    dta[,outcome.var] &lt;- outcome.store
    dta[dta[,treat.var]==1, outcome.var] &lt;- dta[dta[,treat.var]==1, outcome.var] + rbinom(prob = te, size = 1, n=length(dta[dta[,treat.var]==1, outcome.var]))
    dta[,outcome.var] &lt;- ifelse(dta[,outcome.var]&gt;max(outcome.store, na.rm=T), max(outcome.store, na.rm=T), dta[,outcome.var])
    ate.samp.dist[i] &lt;- coef(felm(formula, data=dta))[1]
  }
  
  power &lt;- sum(ate.samp.dist&gt;ate.bar)/sims
  
  coun &lt;- list(&quot;ate.samp.dist&quot; = ate.samp.dist, &quot;power&quot;=power)
  return(coun)
}

lm.ri.power &lt;- function(formula, dta, treat.var, sims, ate.bar, outcome.var, te, ...){
  
  ate.samp.dist &lt;- rep(NA,sims)
  outcome.store &lt;- dta[,outcome.var]
  
  for (i in 1:sims){
    dta[,treat.var] &lt;- dta[,ncol(M)+i]
    dta[,outcome.var] &lt;- outcome.store
    dta[dta[,treat.var]==1, outcome.var] &lt;- dta[dta[,treat.var]==1, outcome.var] + rbinom(prob = te, size = 1, n=length(dta[dta[,treat.var]==1, outcome.var]))
    dta[,outcome.var] &lt;- ifelse(dta[,outcome.var]&gt;max(outcome.store, na.rm=T), max(outcome.store, na.rm=T), dta[,outcome.var])
    ate.samp.dist[i] &lt;- coef(lm(formula, data=dta))[2]
  }
  
  power &lt;- sum(ate.samp.dist&gt;ate.bar)/sims
  
  coun &lt;- list(&quot;ate.samp.dist&quot; = ate.samp.dist, &quot;power&quot;=power)
  return(coun)
}

source2 &lt;- function(file, start, end, ...) {
  file.lines &lt;- scan(file, what=character(), skip=start-1, nlines=end-start+1, sep='\n')
  file.lines.collapsed &lt;- paste(file.lines, collapse='\n')
  source(textConnection(file.lines.collapsed), ...)
} #Function to source parts of main replication file

source('inter.binning.90.R')
source('multiplot.R')

#Function to reset par() show that blocks can be run sequentially and without a plotting device
default.par &lt;- par()


######################################################################################
###Data input
######################################################################################

B &lt;- read.csv(&quot;baseline.csv&quot;, stringsAsFactors=FALSE)
E &lt;- read.csv(&quot;endline.csv&quot;, stringsAsFactors=FALSE)
endline.subjects &lt;- read.csv(&quot;endline_call_list.csv&quot;, row.names=1, stringsAsFactors=FALSE)[,1:4]
recruited.subjects &lt;- read.csv(&quot;recruited_subjects.csv&quot;, stringsAsFactors=FALSE)
smsid &lt;- read.csv(&quot;sms_received_list.csv&quot;, stringsAsFactors=FALSE)
Responsiveness &lt;- read.csv(&quot;digital_responses.csv&quot;, stringsAsFactors=FALSE)
con.mat &lt;- read.csv(&quot;contiguity_mat1.csv&quot;, stringsAsFactors=FALSE)
con2.mat &lt;- read.csv(&quot;contiguity_mat2.csv&quot;, stringsAsFactors=FALSE)
con3.mat &lt;- read.csv(&quot;contiguity_mat3.csv&quot;, stringsAsFactors=FALSE)
#Note the &quot;con.mat&quot; files have village &quot;Kiziiba&quot; manually removed because merged with &quot;Buguro&quot; at cleaning phase
spill.dist &lt;- read.csv(&quot;spillover_matrix.csv&quot;, row.names=1)
loc.master &lt;- read.csv(&quot;location_master.csv&quot;, stringsAsFactors=FALSE)
lc1.planning &lt;- read.csv(&quot;planning_meeting_attendance.csv&quot;, stringsAsFactors=FALSE)
sbj_RI &lt;- read.csv(&quot;assignment_ri.csv&quot;, stringsAsFactors=FALSE)



######################################################################################
###Data setup
######################################################################################

#Removing duplicate subject.id's in endline.subjects
endline.dup &lt;- endline.subjects$Subject_ID_number[duplicated(endline.subjects$Subject_ID_number)]
see &lt;- endline.subjects[endline.subjects$Subject_ID_number %in% endline.dup,]
see &lt;- see[order(see$Subject_ID_number),] #Visual inspection: all subject id's are exact duplicates
M &lt;- endline.subjects[!duplicated(endline.subjects$Subject_ID_number),] #Removing duplicated subject id's
length(unique(M$Subject_ID_number)) #This is the merge point, as all subject id's are unique</code></pre>
<pre><code>## [1] 1723</code></pre>
<pre class="r"><code>#Preparing baseline merge
B &lt;- subset(B, B_subject_refuse_to_participate==&quot;no&quot;) #Removing subjects who refused, no data
length(unique(B$Subject_ID_number)) #There are several duplicate subject id's in the raw baseline file</code></pre>
<pre><code>## [1] 1335</code></pre>
<pre class="r"><code>B.duplicated.list &lt;- B$Subject_ID_number[duplicated(B$Subject_ID_number)] #List of duplicate subject id's in raw endline file
see &lt;- B[B$Subject_ID_number %in% B.duplicated.list,]
see &lt;- see[order(see$Subject_ID_number),]
B &lt;- B[!(B$Subject_ID_number %in% B.duplicated.list),]
M &lt;- merge(M,B, by=&quot;Subject_ID_number&quot;, all.x=TRUE) #still 1723 observations

#Preparing endline merge
E &lt;- subset(E, E_1_Did_the_subject_refuse_to_p==&quot;no&quot;) #Removing subjects who refused, no data
E.duplicated.list &lt;- E$Subject_ID_number[duplicated(E$Subject_ID_number)] #List of duplicate subject id's in raw endline file
see &lt;- E[E$Subject_ID_number %in% E.duplicated.list,]
see &lt;- see[order(see$Subject_ID_number),]
E &lt;- E[!(E$Subject_ID_number %in% E.duplicated.list),]
M &lt;- merge(M,E, by=&quot;Subject_ID_number&quot;, all.x=TRUE) #still 1723 observations

#Merging treatment status and block from the assignment file
treat.dta &lt;- recruited.subjects[,c(1,10,11,12)] #Getting subcounty, treatment status, and block
M &lt;- merge(M,treat.dta, by=&quot;Subject_ID_number&quot;, all.x=TRUE) #still 1723 observations
M$subcounty.blocking &lt;- ifelse(M$subcounty.blocking==&quot;Rutenga&quot;, &quot;Singles&quot;, M$subcounty.blocking) #Because of exclusion, Rutenga subcounty has only one village, moving to &quot;Singles&quot; block

#TextIt data
m7&lt;-table(smsid$Subject_ID_number)
m7&lt;-data.frame(m7)
names(m7)&lt;-c(&quot;Subject_ID_number&quot;,&quot;participate.m7&quot;)
M &lt;- merge(M,m7, by=&quot;Subject_ID_number&quot;, all.x=TRUE) #still 1723 observations

#Location cleaning
villages &lt;- loc.master$village.updated
for (i in 1:length(villages)){
  villages[i] &lt;- ifelse(loc.master$village.cleaned[i]==&quot;&quot;, loc.master$village.updated[i], loc.master$village.cleaned[i])
}
villages[duplicated(villages)] #Checking for errors; looks good though some original errors resulted in villages with mixed treatment assignment (Kajumo, Omumbuga)</code></pre>
<pre><code>## [1] &quot;Kinaba&quot;   &quot;Bugoro&quot;   &quot;Kajumo&quot;   &quot;Omumbuga&quot;</code></pre>
<pre class="r"><code>see &lt;- subset(loc.master,excluded==0)
for (i in 1:length(villages)){
  villages[i] &lt;- ifelse(see$village.cleaned[i]==&quot;&quot;, see$village.updated[i], see$village.cleaned[i])
}
villages[duplicated(villages)] #Checking for errors; looks good though some original errors resulted in villages with mixed treatment assignment (Kajumo, Omumbuga)</code></pre>
<pre><code>##  [1] &quot;Bugoro&quot;   &quot;Kajumo&quot;   &quot;Omumbuga&quot; NA         NA         NA        
##  [7] NA         NA         NA         NA         NA         NA        
## [13] NA</code></pre>
<pre class="r"><code>see$final.id &lt;- ifelse(see$location.id.cleaned==&quot;&quot;,see$location.id,see$location.id.cleaned)
unique(see$final.id)</code></pre>
<pre><code>##  [1] &quot;Kanungu.Kirima.Bushura.Ahabutare&quot;                      
##  [2] &quot;Rubanda.Ikumba.Mushanje.Bigandu&quot;                       
##  [3] &quot;Rubanda.Ruhija.Kitojo.Bishayu&quot;                         
##  [4] &quot;Rubanda.Ruhija.Kashekyera.Bitanwa&quot;                     
##  [5] &quot;Kanungu.Kirima.Bushura.Bugandaro&quot;                      
##  [6] &quot;Kanungu.Kinaaba.Kiziiba.Bugoro&quot;                        
##  [7] &quot;Kanungu.Kirima.Rutugunda.Buhamba&quot;                      
##  [8] &quot;Kanungu.Kayonza.Mukono.Buhoma&quot;                         
##  [9] &quot;Kanungu.Kanungu Town Council.Southern Ward.Butare&quot;     
## [10] &quot;Rubanda.Ruhija.Buhumuriro.Buzaniro&quot;                    
## [11] &quot;Kanungu.Mpungu.Buremba.Bweyongyezo&quot;                    
## [12] &quot;Kanungu.Kayonza.Bujengwe.Byumba&quot;                       
## [13] &quot;Rubanda.Ikumba.Kashasha.Ihunga&quot;                        
## [14] &quot;Rubanda.Ruhija.Kashekyera.Inywero&quot;                     
## [15] &quot;Kanungu.Kayonza.Mukono.Iraaro&quot;                         
## [16] &quot;Kanungu.Kinaaba.Mukirwa.Ishaya&quot;                        
## [17] &quot;Rubanda.Ruhija.Kiyebe.Kabere&quot;                          
## [18] &quot;Kanungu.Kayonza.Bujengwe.Kacerere&quot;                     
## [19] &quot;Rubanda.Ikumba.Nyamabare.Kachwamuhoro&quot;                 
## [20] &quot;Rubanda.Ikumba.Kashasha.Kagogo&quot;                        
## [21] &quot;Kanungu.Kayonza.Karangara.Kagoma&quot;                      
## [22] &quot;Kisoro.Nyabwishenya.Nteko.Kahurire&quot;                    
## [23] &quot;Kanungu.Kirima.Bushura.Kajumo&quot;                         
## [24] &quot;Kanungu.Butogota town council.Eastern ward.Kanyabuhama&quot;
## [25] &quot;Kisoro.Kirundo.Rubuguri.Kanyamahene&quot;                   
## [26] &quot;Kanungu.Mpungu.Mpungu.Kanyashogi&quot;                      
## [27] &quot;Kanungu.Mpungu.Muramba.Karambi&quot;                        
## [28] &quot;Kanungu.Mpungu.Buremba.Karukara&quot;                       
## [29] &quot;Kanungu.Kanungu Town Council.Southern Ward.Kashenyi&quot;   
## [30] &quot;Kisoro.Kirundo.Rubuguri.Kashija&quot;                       
## [31] &quot;Kanungu.Kirima.Rubimbwa.Kasoni&quot;                        
## [32] &quot;Rubanda.Ikumba.Kashasha.Katojo&quot;                        
## [33] &quot;Rubanda.Ruhija.Kitojo.Katoma&quot;                          
## [34] &quot;Kanungu.Mpungu.Buremba.Katunda&quot;                        
## [35] &quot;Kanungu.Kayonza.Bujengwe.Kazahi&quot;                       
## [36] &quot;Kanungu.Kirima.Bushura.Kazuru&quot;                         
## [37] &quot;Kanungu.Mpungu.Muramba.Kibingo&quot;                        
## [38] &quot;Kanungu.Mpungu.Ngaara.Kigaga&quot;                          
## [39] &quot;Rubanda.Ikumba.Nyamabare.Kigarama&quot;                     
## [40] &quot;Rubanda.Ikumba.Mushanje.Kigumira&quot;                      
## [41] &quot;Kanungu.Rutenga.Muramba.Kijuma&quot;                        
## [42] &quot;Kisoro.Nyabwishenya.Nteko.Kikobero&quot;                    
## [43] &quot;Rubanda.Ikumba.Mushanje.Kinyungu&quot;                      
## [44] &quot;Rubanda.Ikumba.Kashasha.Kiriba A&quot;                      
## [45] &quot;Rubanda.Ikumba.Kashasha.Kiriba B&quot;                      
## [46] &quot;Kanungu.Kayonza.Bujengwe.Kishegyere&quot;                   
## [47] &quot;Kanungu.Mpungu.Buremba.Kitahurira (Hakikome)&quot;          
## [48] &quot;Rubanda.Ikumba.Kashasha.Kitahurira (Kashasha)&quot;         
## [49] &quot;Kanungu.Kirima.Rubimbwa.Kitunga&quot;                       
## [50] &quot;Kanungu.Kanungu Town Council.Southern Ward.Kyabworo&quot;   
## [51] &quot;Rubanda.Ruhija.Ntungamo.Kyeitokwa&quot;                     
## [52] &quot;Rubanda.Ruhija.Kashekyera.Kyogo (Rubanda)&quot;             
## [53] &quot;Kanungu.Mpungu.Ngaara.Kyogo (Kanungu)&quot;                 
## [54] &quot;Kanungu.Kayonza.Mukono.Kyumbugushu&quot;                    
## [55] &quot;Rubanda.Ikumba.Kashasha.Mashoho&quot;                       
## [56] &quot;Rubanda.Ruhija.Kiyebe.Mataka&quot;                          
## [57] &quot;Rubanda.Ruhija.Buhumuriro.Mburameizi&quot;                  
## [58] &quot;Kanungu.Kayonza.Mukono.Mukono&quot;                         
## [59] &quot;Kisoro.Nyabwishenya.Nteko.Murore&quot;                      
## [60] &quot;Rubanda.Ikumba.Kashasha.Murubaya&quot;                      
## [61] &quot;Kanungu.Mpungu.Mpungu.Murushasha&quot;                      
## [62] &quot;Kanungu.Kayonza.Karangara.Musheija&quot;                    
## [63] &quot;Kanungu.Kayonza.Bujengwe.Mushorero&quot;                    
## [64] &quot;Kanungu.Kirima.Bushura.Mutojo&quot;                         
## [65] &quot;Rubanda.Ikumba.Kashasha.Ndego&quot;                         
## [66] &quot;Kanungu.Kayonza.Mukono.Nkwenda&quot;                        
## [67] &quot;Kisoro.Kirundo.Rubuguri.Nombe&quot;                         
## [68] &quot;Kisoro.Nyabwishenya.Nteko.Nteko&quot;                       
## [69] &quot;Rubanda.Ruhija.Ntungamo.Ntungamo&quot;                      
## [70] &quot;Kisoro.Kirundo.Rubuguri.Nyabaremura&quot;                   
## [71] &quot;Rubanda.Ruhija.Buhumuriro.Nyabiha&quot;                     
## [72] &quot;Kanungu.Kayonza.Karangara.Nyakabingo&quot;                  
## [73] &quot;Kanungu.Kirima.Bushura.Nyakabungo (Bushura)&quot;           
## [74] &quot;Rubanda.Ruhija.Kashekyera.Nyakabungo (Kashekyera)&quot;     
## [75] &quot;Kanungu.Kirima.Rubimbwa.Nyakabungo (Rubimbwa)&quot;         
## [76] &quot;Rubanda.Ruhija.Kiyebe.Nyakaranga&quot;                      
## [77] &quot;Rubanda.Ikumba.Nyamabare.Nyamabale&quot;                    
## [78] &quot;Kisoro.Bukimbiri.Remera.Nyamatsinda&quot;                   
## [79] &quot;Kanungu.Kayonza.Bujengwe.Nyamishamba&quot;                  
## [80] &quot;Kanungu.Mpungu.Mpungu.Nyamizo&quot;                         
## [81] &quot;Kanungu.Kanungu Town Council.Southern Ward.Omumbuga&quot;   
## [82] &quot;Kanungu.Kayonza.Mukono.Rubona&quot;                         
## [83] &quot;Rubanda.Ruhija.Ntungamo.Rugandu&quot;                       
## [84] &quot;Kanungu.Kirima.Rubimbwa.Ruheza&quot;                        
## [85] &quot;Kanungu.Mpungu.Buremba.Rukungwe&quot;                       
## [86] &quot;Kisoro.Kirundo.Rubuguri.Rushaga&quot;                       
## [87] &quot;Rubanda.Ikumba.Mushanje.Rwaburegyeya&quot;                  
## [88] &quot;Kanungu.Kayonza.Karangara.Rwamiyumbu&quot;                  
## [89] &quot;Kanungu.Kirima.Bushura.Rwengwe&quot;                        
## [90] &quot;Rubanda.Ruhija.Kitojo.Rwensanziro&quot;                     
## [91] &quot;Rubanda.Muko.Kaara.Ryamihanda&quot;</code></pre>
<pre class="r"><code>#unique(M$Updated.Village)[!(unique(M$Updated.Village) %in% villages)]
M$Updated.Village &lt;- ifelse(M$Updated.Village==&quot;Katooma&quot;,&quot;Katoma&quot;,M$Updated.Village)
M$Updated.Village &lt;- ifelse(M$Updated.Village==&quot;Kijumwa&quot;,&quot;Kijuma&quot;,M$Updated.Village)
M$Updated.Village &lt;- ifelse(M$Updated.Village==&quot;Kyogo (Kabale)&quot;,&quot;Kyogo (Rubanda)&quot;,M$Updated.Village)
M$Updated.Village &lt;- ifelse(M$Updated.Village==&quot;Mushija&quot;,&quot;Musheija&quot;,M$Updated.Village)
M$Updated.Village &lt;- ifelse(M$Updated.Village==&quot;Ndeego&quot;,&quot;Ndego&quot;,M$Updated.Village)

#Adding variables to probe spillover
spill.village.list &lt;- names(spill.dist)
spill.village.list[!(spill.village.list %in% recruited.subjects$updated.village)] #list in spillover matrix that don't match</code></pre>
<pre><code>##  [1] &quot;Kashija..Rubuguri&quot;     &quot;Higabiro..Rubuguri&quot;   
##  [3] &quot;Nteeko&quot;                &quot;Kacwamuhoro&quot;          
##  [5] &quot;Nyakaranaga&quot;           &quot;Byamihanda&quot;           
##  [7] &quot;kagogo&quot;                &quot;Kiriba.A&quot;             
##  [9] &quot;Kiriba.B&quot;              &quot;Ndego&quot;                
## [11] &quot;Kitahurira&quot;            &quot;Rwensaniro&quot;           
## [13] &quot;Rugando&quot;               &quot;Ntunagamo&quot;            
## [15] &quot;Katoma&quot;                &quot;Bishanyu&quot;             
## [17] &quot;Nyakabungo&quot;            &quot;Buzaniza&quot;             
## [19] &quot;Mburamaizi&quot;            &quot;Kyogo..Kabale.&quot;       
## [21] &quot;Kyogo..Kanungu.&quot;       &quot;Nyakabungo..Rubimbwa.&quot;
## [23] &quot;Nyakabungo.Bushura.&quot;   &quot;Kiziba&quot;               
## [25] &quot;Ishaya..Mucusye.&quot;      &quot;Bushegyenyi&quot;          
## [27] &quot;Kyibingo&quot;              &quot;Kitahulira&quot;</code></pre>
<pre class="r"><code>#con.village.list &lt;- con.mat$Village..m.
#con.village.list[!(con.village.list %in% M$Updated.Village)] #list in contiguity matrix that don't match M village names
#con.village.list[!(con.village.list %in% loc.master$village.updated)] #list in contiguity matrix that don't match location master original names

#Cleaning the con.mat file for village names, see email thread &quot;village names in matrices&quot; from Jan-2017
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kashija/ Rubuguri&quot;,&quot;Kashija&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Higabiro/ Rubuguri&quot;,&quot;Higabiro (Rubuguri)&quot;,con.mat$Village..m.)
#Higabiro (Rubuguri): this is an RS village, but we do not have any subjects from this village
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Nteeko&quot;,&quot;Nteko&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Nyakabungo( Rubimbwa)&quot;,&quot;Nyakabungo (Rubimbwa)&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Nyakabungo(Bushura)&quot;,&quot;Nyakabungo (Bushura)&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Ishaya (Mucusye)&quot;,&quot;Ishaya&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kiziba&quot;,&quot;Kiziiba&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Bushegyenyi&quot;,&quot;Bushegenyi&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kyibingo&quot;,&quot;Kibingo&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kitahulira&quot;,&quot;Kitahurira (Hakikome)&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Nyakabungo&quot;,&quot;Nyakabungo (Kashekyera)&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Buzaniza&quot;,&quot;Buzaniro&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Mburamaizi&quot;,&quot;Mburameizi&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Bishanyu&quot;,&quot;Bishayu&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Katooma&quot;,&quot;Katoma&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Rwensaniro&quot;,&quot;Rwensanziro&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Rugando&quot;,&quot;Rugandu&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Ntunagamo&quot;,&quot;Ntungamo&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Nyakaranaga&quot;,&quot;Nyakaranga&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kacwamuhoro&quot;,&quot;Kachwamuhoro&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kitahurira&quot;,&quot;Kitahurira (Kashasha)&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Ndeego&quot;,&quot;Ndego&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;kagogo&quot;,&quot;Kagogo&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Byamihanda&quot;,&quot;Ryamihanda&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Mushija&quot;,&quot;Musheija&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kijumwa&quot;,&quot;Kijuma&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kiziiba&quot;,&quot;Bugoro&quot;,con.mat$Village..m.)
con.mat$Village..m. &lt;- ifelse(con.mat$Village..m.==&quot;Kyogo (Kabale)&quot;,&quot;Kyogo (Rubanda)&quot;,con.mat$Village..m.)

#Cleaning the con2.mat file for village names, see email thread &quot;village names in matrices&quot; from Jan-2017
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kashija/ Rubuguri&quot;,&quot;Kashija&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Higabiro/ Rubuguri&quot;,&quot;Higabiro (Rubuguri)&quot;,con2.mat$Village..m.)
#Higabiro (Rubuguri): this is an RS village, but we do not have any subjects from this village
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Nteeko&quot;,&quot;Nteko&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Nyakabungo( Rubimbwa)&quot;,&quot;Nyakabungo (Rubimbwa)&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Nyakabungo(Bushura)&quot;,&quot;Nyakabungo (Bushura)&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Ishaya (Mucusye)&quot;,&quot;Ishaya&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kiziba&quot;,&quot;Kiziiba&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Bushegyenyi&quot;,&quot;Bushegenyi&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kyibingo&quot;,&quot;Kibingo&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kitahulira&quot;,&quot;Kitahurira (Hakikome)&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Nyakabungo&quot;,&quot;Nyakabungo (Kashekyera)&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Buzaniza&quot;,&quot;Buzaniro&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Mburamaizi&quot;,&quot;Mburameizi&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Bishanyu&quot;,&quot;Bishayu&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Katooma&quot;,&quot;Katoma&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Rwensaniro&quot;,&quot;Rwensanziro&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Rugando&quot;,&quot;Rugandu&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Ntunagamo&quot;,&quot;Ntungamo&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Nyakaranaga&quot;,&quot;Nyakaranga&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kacwamuhoro&quot;,&quot;Kachwamuhoro&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kitahurira&quot;,&quot;Kitahurira (Kashasha)&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Ndeego&quot;,&quot;Ndego&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;kagogo&quot;,&quot;Kagogo&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Byamihanda&quot;,&quot;Ryamihanda&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Mushija&quot;,&quot;Musheija&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kijumwa&quot;,&quot;Kijuma&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kiziiba&quot;,&quot;Bugoro&quot;,con2.mat$Village..m.)
con2.mat$Village..m. &lt;- ifelse(con2.mat$Village..m.==&quot;Kyogo (Kabale)&quot;,&quot;Kyogo (Rubanda)&quot;,con2.mat$Village..m.)

#Cleaning the con3.mat file for village names, see email thread &quot;village names in matrices&quot; from Jan-2017
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kashija/ Rubuguri&quot;,&quot;Kashija&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Higabiro/ Rubuguri&quot;,&quot;Higabiro (Rubuguri)&quot;,con3.mat$Village..m.)
#Higabiro (Rubuguri): this is an RS village, but we do not have any subjects from this village
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Nteeko&quot;,&quot;Nteko&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Nyakabungo( Rubimbwa)&quot;,&quot;Nyakabungo (Rubimbwa)&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Nyakabungo(Bushura)&quot;,&quot;Nyakabungo (Bushura)&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Ishaya (Mucusye)&quot;,&quot;Ishaya&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kiziba&quot;,&quot;Kiziiba&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Bushegyenyi&quot;,&quot;Bushegenyi&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kyibingo&quot;,&quot;Kibingo&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kitahulira&quot;,&quot;Kitahurira (Hakikome)&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Nyakabungo&quot;,&quot;Nyakabungo (Kashekyera)&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Buzaniza&quot;,&quot;Buzaniro&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Mburamaizi&quot;,&quot;Mburameizi&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Bishanyu&quot;,&quot;Bishayu&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Katooma&quot;,&quot;Katoma&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Rwensaniro&quot;,&quot;Rwensanziro&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Rugando&quot;,&quot;Rugandu&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Ntunagamo&quot;,&quot;Ntungamo&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Nyakaranaga&quot;,&quot;Nyakaranga&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kacwamuhoro&quot;,&quot;Kachwamuhoro&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kitahurira&quot;,&quot;Kitahurira (Kashasha)&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Ndeego&quot;,&quot;Ndego&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;kagogo&quot;,&quot;Kagogo&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Byamihanda&quot;,&quot;Ryamihanda&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Mushija&quot;,&quot;Musheija&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kijumwa&quot;,&quot;Kijuma&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kiziiba&quot;,&quot;Bugoro&quot;,con3.mat$Village..m.)
con3.mat$Village..m. &lt;- ifelse(con3.mat$Village..m.==&quot;Kyogo (Kabale)&quot;,&quot;Kyogo (Rubanda)&quot;,con3.mat$Village..m.)

#Cleaning loc.master names into one vector
loc.master$village.final &lt;- ifelse(loc.master$village.cleaned==&quot;&quot;, loc.master$village.updated, loc.master$village.cleaned)
loc.master$village.final &lt;- ifelse(loc.master$village.final==&quot;Kikangaga &quot;, &quot;Kikangaga&quot;, loc.master$village.final)

#Checking the location.id cleaning in all files
con.mat$Village..m.[!(con.mat$Village..m. %in% loc.master$village.final)] #Notes: only Higabiro (Rubuguri), no subjects in study</code></pre>
<pre><code>## [1] &quot;Higabiro (Rubuguri)&quot;</code></pre>
<pre class="r"><code>loc.master$village.final[!(loc.master$village.final %in% con.mat$Village..m.)] #Notes: &quot;Kinaba&quot; &quot;Kinaba&quot; &quot;Kyogo&quot;  &quot;Rukere&quot; all excluded</code></pre>
<pre><code>## [1] &quot;Kinaba&quot; &quot;Kinaba&quot; &quot;Kyogo&quot;  &quot;Rukere&quot;</code></pre>
<pre class="r"><code>unique(M$Updated.Village) %in% loc.master$village.final #Note: all appear in cleaned loc.master</code></pre>
<pre><code>##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE</code></pre>
<pre class="r"><code>unique(M$Updated.Village)[!(unique(M$Updated.Village) %in% con.mat$Village..m.)] #&quot;Kinaba&quot; does not specify which one, remove Kinaba from dataframe?</code></pre>
<pre><code>## [1] &quot;Kinaba&quot;</code></pre>
<pre class="r"><code>#Correcting Kinaba village names (redistricted), for those we were able to reach, see &quot;Corrected villages.xls&quot; and email thread &quot;Bwindi Village Confusion&quot;
M$Updated.Village[M$Subject_ID_number %in% c(2581,2583,2587)] &lt;- &quot;Nyabiha&quot;
M$Updated.Village[M$Subject_ID_number %in% c(2518)] &lt;- &quot;Kyabworo&quot;
M &lt;- M[M$Updated.Village!=&quot;Kinaba&quot;,] #Removing &quot;Kinaba&quot; from dataframe per exclusion from revenue sharing during '16/'17 round

#Creating spillover indicator in M
treated.villages &lt;- loc.master$village.final[loc.master$treat==1]
#Note: rows/columns in con.mat must be ordered identically, code below checks
#col.n &lt;- colnames(con.mat)[-1]
#ex &lt;- dataframe(con.mat$Village..m.,col.n)
#con.mat$Village..m.[48] &lt;- &quot;Kiziba&quot; #Correction discussed in 2/20/17 email to Colleen

for (i in 1:nrow(M)){
  row.test &lt;- con.mat[M$Updated.Village[i]==con.mat$Village..m.,-1]==1
  row.test2 &lt;- con2.mat[M$Updated.Village[i]==con2.mat$Village..m.,-1]==1
  row.test3 &lt;- con3.mat[M$Updated.Village[i]==con3.mat$Village..m.,-1]==1
  con.villages &lt;- con.mat$Village..m.[row.test]
  con2.villages &lt;- con2.mat$Village..m.[row.test2]
  con3.villages &lt;- con3.mat$Village..m.[row.test3]
  M$S1[i] &lt;- sum(con.villages %in% treated.villages)
  M$S2[i] &lt;- sum(con2.villages %in% treated.villages)
  M$S3[i] &lt;- sum(con3.villages %in% treated.villages)
  
}
M$S1 &lt;- as.factor(M$S1)
M$S2 &lt;- as.factor(M$S2)
M$S3 &lt;- as.factor(M$S3)

#Treatment Density (number of subjects per village)
for (i in 1:nrow(M)){
  sub &lt;- subset(M, Updated.Village==M$Updated.Village[i])
  M$subjects.per.village[i] &lt;- nrow(sub)
}

#Attrition
M$attr &lt;- ifelse(is.na(M$E_id), 1, 0)

#Setting up RI object
source('outcome_var_mean_imp_setup.R')
sbj_RI &lt;- sbj_RI[,c(1,5:ncol(sbj_RI))] #selecting only the treatment status to avoid introducing duplicate data to M
M.ri &lt;- merge(M, sbj_RI, by=&quot;Subject_ID_number&quot;, all.x=TRUE)

######################################################################################
###Tables D1-3. Results for F-Test of Improved Model Fit with Spillover Indicators
#Note: models presented in this section exclude most covariates
######################################################################################
#Note: tables formatted manually based on values displayed below

source('outcome_var_mean_imp_setup.R')

###H7.1: Satisfaction with RS Info from UWA
mod.h7 &lt;- lm(e.knowledge.b7 ~ treat + b.knowledge.b7 + subcounty.blocking, data=M)

mod.h7.spill &lt;- lm(e.knowledge.b7 ~ treat + S1 + b.knowledge.b7 + subcounty.blocking, data=M)

mod.h7.spill2 &lt;- lm(e.knowledge.b7 ~ treat + S2 + b.knowledge.b7 + subcounty.blocking, data=M)

mod.h7.spill3 &lt;- lm(e.knowledge.b7 ~ treat + S3 + b.knowledge.b7 + subcounty.blocking, data=M)

###H7.2: Know How RS Works Generally
mod.h7.2 &lt;- lm(e.knowledge.b8 ~ treat + b.knowledge.b8 + subcounty.blocking, data=M)

mod.h7.2.spill &lt;- lm(e.knowledge.b8 ~ treat + S1 + b.knowledge.b8 + subcounty.blocking, data=M)

mod.h7.2.spill2 &lt;- lm(e.knowledge.b8 ~ treat + S2 + b.knowledge.b8 + subcounty.blocking, data=M)

mod.h7.2.spill3 &lt;- lm(e.knowledge.b8 ~ treat + S3 + b.knowledge.b8 + subcounty.blocking, data=M)

###H7.3: How RS Works in my Village
mod.h7.3 &lt;- lm(e.knowledge.b9 ~ treat + b.knowledge.b9 + subcounty.blocking, data=M)

mod.h7.3.spill &lt;- lm(e.knowledge.b9 ~ treat + S1 + b.knowledge.b9 + subcounty.blocking, data=M)

mod.h7.3.spill2 &lt;- lm(e.knowledge.b9 ~ treat + S2 + b.knowledge.b9 + subcounty.blocking, data=M)

mod.h7.3.spill3 &lt;- lm(e.knowledge.b9 ~ treat + S3 + b.knowledge.b9 + subcounty.blocking, data=M)

###H8.1: Opportunities to Participate in Planning
mod.h8.1 &lt;- lm(e.participate.b10 ~ treat + b.participate.b10 + subcounty.blocking, data=M)

mod.h8.1.spill &lt;- lm(e.participate.b10 ~ treat + S1 + b.participate.b10 + subcounty.blocking, data=M)

mod.h8.1.spill2 &lt;- lm(e.participate.b10 ~ treat + S2 + b.participate.b10 + subcounty.blocking, data=M)

mod.h8.1.spill3 &lt;- lm(e.participate.b10 ~ treat + S3 + b.participate.b10 + subcounty.blocking, data=M)

###H8.3: Participate in RS meetings in past months
mod.h8.3 &lt;- lm(e.participate.e4 ~ treat + subcounty.blocking, data=M)

mod.h8.3.spill &lt;- lm(e.participate.e4 ~ treat + S1 + subcounty.blocking, data=M)

mod.h8.3.spill2 &lt;- lm(e.participate.e4 ~ treat + S2 + subcounty.blocking, data=M)

mod.h8.3.spill3 &lt;- lm(e.participate.e4 ~ treat + S3 + subcounty.blocking, data=M)

###H9.1: Satisfaction with opportunities to communicate with UWA about RS
mod.h9.1 &lt;- lm(e.participate.b11 ~ treat + b.participate.b11 + subcounty.blocking, data=M)

mod.h9.1.spill &lt;- lm(e.participate.b11 ~ treat + S1 + b.participate.b11 + subcounty.blocking, data=M)

mod.h9.1.spill2 &lt;- lm(e.participate.b11 ~ treat + S2 + b.participate.b11 + subcounty.blocking, data=M)

mod.h9.1.spill3 &lt;- lm(e.participate.b11 ~ treat + S3 + b.participate.b11 + subcounty.blocking, data=M)

###H9.2: Know Right Person to Contact
mod.h9.2 &lt;- lm(e.participate.b12 ~ treat + b.participate.b12 + subcounty.blocking, data=M)

mod.h9.2.spill &lt;- lm(e.participate.b12 ~ treat + S1 + b.participate.b12 + subcounty.blocking, data=M)

mod.h9.2.spill2 &lt;- lm(e.participate.b12 ~ treat + S2 + b.participate.b12 + subcounty.blocking, data=M)

mod.h9.2.spill3 &lt;- lm(e.participate.b12 ~ treat + S3 + b.participate.b12 + subcounty.blocking, data=M)

###H10: Satisfaction with Park Management
nod.h10mgmt &lt;- lm(e.satisfaction.b2 ~ treat + b.satisfaction.b2 + subcounty.blocking, data=M)

nod.h10mgmt.spill &lt;- lm(e.satisfaction.b2 ~ treat + S1 + b.satisfaction.b2 + subcounty.blocking, data=M)

nod.h10mgmt.spill2 &lt;- lm(e.satisfaction.b2 ~ treat + S2 + b.satisfaction.b2 + subcounty.blocking, data=M)

nod.h10mgmt.spill3 &lt;- lm(e.satisfaction.b2 ~ treat + S3 + b.satisfaction.b2 + subcounty.blocking, data=M)

###H11: Satisfaction with RS
nod.h11 &lt;- lm(e.satisfaction.b3 ~ treat + b.satisfaction.b3 + subcounty.blocking, data=M)

nod.h11.spill &lt;- lm(e.satisfaction.b3 ~ treat + S1 + b.satisfaction.b3 + subcounty.blocking, data=M)

nod.h11.spill2 &lt;- lm(e.satisfaction.b3 ~ treat + S2 + b.satisfaction.b3 + subcounty.blocking, data=M)

nod.h11.spill3 &lt;- lm(e.satisfaction.b3 ~ treat + S3 + b.satisfaction.b3 + subcounty.blocking, data=M)

###H13: Support for Conservation (Importance of Protecting Bwindi)
nod.h13 &lt;- lm(e.conservation.b6 ~ treat + b.conservation.b6 + subcounty.blocking, data=M)

nod.h13.spill &lt;- lm(e.conservation.b6 ~ treat + S1 + b.conservation.b6 + subcounty.blocking, data=M)

nod.h13.spill2 &lt;- lm(e.conservation.b6 ~ treat + S2 + b.conservation.b6 + subcounty.blocking, data=M)

nod.h13.spill3 &lt;- lm(e.conservation.b6 ~ treat + S3 + b.conservation.b6 + subcounty.blocking, data=M)

#Table D1
anova(nod.h13, nod.h13.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.conservation.b6 ~ treat + b.conservation.b6 + subcounty.blocking
## Model 2: e.conservation.b6 ~ treat + S1 + b.conservation.b6 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    986 105.47                           
## 2    984 105.40  2  0.065152 0.3041 0.7378</code></pre>
<pre class="r"><code>anova(nod.h11, nod.h11.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b3 ~ treat + b.satisfaction.b3 + subcounty.blocking
## Model 2: e.satisfaction.b3 ~ treat + S1 + b.satisfaction.b3 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)  
## 1    980 1144.0                             
## 2    978 1133.3  2    10.654 4.5972 0.0103 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>anova(nod.h10mgmt, nod.h10mgmt.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b2 ~ treat + b.satisfaction.b2 + subcounty.blocking
## Model 2: e.satisfaction.b2 ~ treat + S1 + b.satisfaction.b2 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    984 959.12                           
## 2    982 958.63  2   0.49038 0.2512 0.7779</code></pre>
<pre class="r"><code>anova(mod.h9.2, mod.h9.2.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b12 ~ treat + b.participate.b12 + subcounty.blocking
## Model 2: e.participate.b12 ~ treat + S1 + b.participate.b12 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq     F Pr(&gt;F)
## 1    971 572.80                          
## 2    969 572.53  2   0.26827 0.227 0.7969</code></pre>
<pre class="r"><code>anova(mod.h9.1, mod.h9.1.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b11 ~ treat + b.participate.b11 + subcounty.blocking
## Model 2: e.participate.b11 ~ treat + S1 + b.participate.b11 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    980 1140.1                           
## 2    978 1139.5  2   0.65093 0.2794 0.7563</code></pre>
<pre class="r"><code>anova(mod.h8.3, mod.h8.3.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.e4 ~ treat + subcounty.blocking
## Model 2: e.participate.e4 ~ treat + S1 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F  Pr(&gt;F)  
## 1    988 189.90                              
## 2    986 188.93  2    0.9786 2.5536 0.07831 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>anova(mod.h8.1, mod.h8.1.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b10 ~ treat + b.participate.b10 + subcounty.blocking
## Model 2: e.participate.b10 ~ treat + S1 + b.participate.b10 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    982 823.78                           
## 2    980 821.67  2    2.1121 1.2595 0.2842</code></pre>
<pre class="r"><code>anova(mod.h7.3, mod.h7.3.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b9 ~ treat + b.knowledge.b9 + subcounty.blocking
## Model 2: e.knowledge.b9 ~ treat + S1 + b.knowledge.b9 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq     F Pr(&gt;F)
## 1    979 440.21                          
## 2    977 439.75  2   0.45552 0.506  0.603</code></pre>
<pre class="r"><code>anova(mod.h7.2, mod.h7.2.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b8 ~ treat + b.knowledge.b8 + subcounty.blocking
## Model 2: e.knowledge.b8 ~ treat + S1 + b.knowledge.b8 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    974 508.43                           
## 2    972 507.87  2   0.55136 0.5276 0.5902</code></pre>
<pre class="r"><code>anova(mod.h7, mod.h7.spill)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b7 ~ treat + b.knowledge.b7 + subcounty.blocking
## Model 2: e.knowledge.b7 ~ treat + S1 + b.knowledge.b7 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    985 829.96                           
## 2    983 828.85  2    1.1068 0.6563  0.519</code></pre>
<pre class="r"><code>#Table D2
anova(nod.h13, nod.h13.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.conservation.b6 ~ treat + b.conservation.b6 + subcounty.blocking
## Model 2: e.conservation.b6 ~ treat + S2 + b.conservation.b6 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    986 105.47                           
## 2    982 105.12  4   0.35246 0.8232 0.5104</code></pre>
<pre class="r"><code>anova(nod.h11, nod.h11.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b3 ~ treat + b.satisfaction.b3 + subcounty.blocking
## Model 2: e.satisfaction.b3 ~ treat + S2 + b.satisfaction.b3 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F  Pr(&gt;F)  
## 1    980 1144.0                              
## 2    976 1129.4  4    14.546 3.1427 0.01398 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>anova(nod.h10mgmt, nod.h10mgmt.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b2 ~ treat + b.satisfaction.b2 + subcounty.blocking
## Model 2: e.satisfaction.b2 ~ treat + S2 + b.satisfaction.b2 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq    F Pr(&gt;F)
## 1    984 959.12                         
## 2    980 954.09  4    5.0237 1.29 0.2721</code></pre>
<pre class="r"><code>anova(mod.h9.2, mod.h9.2.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b12 ~ treat + b.participate.b12 + subcounty.blocking
## Model 2: e.participate.b12 ~ treat + S2 + b.participate.b12 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    971 572.80                           
## 2    967 569.36  4    3.4321 1.4572 0.2132</code></pre>
<pre class="r"><code>anova(mod.h9.1, mod.h9.1.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b11 ~ treat + b.participate.b11 + subcounty.blocking
## Model 2: e.participate.b11 ~ treat + S2 + b.participate.b11 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    980 1140.1                           
## 2    976 1131.8  4    8.2947 1.7882  0.129</code></pre>
<pre class="r"><code>anova(mod.h8.3, mod.h8.3.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.e4 ~ treat + subcounty.blocking
## Model 2: e.participate.e4 ~ treat + S2 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    988 189.90                           
## 2    984 189.72  4   0.18824 0.2441 0.9133</code></pre>
<pre class="r"><code>anova(mod.h8.1, mod.h8.1.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b10 ~ treat + b.participate.b10 + subcounty.blocking
## Model 2: e.participate.b10 ~ treat + S2 + b.participate.b10 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq     F Pr(&gt;F)
## 1    982 823.78                          
## 2    978 820.41  4    3.3722 1.005 0.4039</code></pre>
<pre class="r"><code>anova(mod.h7.3, mod.h7.3.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b9 ~ treat + b.knowledge.b9 + subcounty.blocking
## Model 2: e.knowledge.b9 ~ treat + S2 + b.knowledge.b9 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    979 440.21                           
## 2    975 438.94  4    1.2611 0.7003 0.5918</code></pre>
<pre class="r"><code>anova(mod.h7.2, mod.h7.2.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b8 ~ treat + b.knowledge.b8 + subcounty.blocking
## Model 2: e.knowledge.b8 ~ treat + S2 + b.knowledge.b8 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    974 508.43                           
## 2    970 506.19  4    2.2395 1.0729 0.3686</code></pre>
<pre class="r"><code>anova(mod.h7, mod.h7.spill2)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b7 ~ treat + b.knowledge.b7 + subcounty.blocking
## Model 2: e.knowledge.b7 ~ treat + S2 + b.knowledge.b7 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    985 829.96                           
## 2    981 827.18  4    2.7842 0.8255  0.509</code></pre>
<pre class="r"><code>#Table D3
anova(nod.h13, nod.h13.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.conservation.b6 ~ treat + b.conservation.b6 + subcounty.blocking
## Model 2: e.conservation.b6 ~ treat + S3 + b.conservation.b6 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    986 105.47                           
## 2    981 104.78  5    0.6927 1.2971 0.2628</code></pre>
<pre class="r"><code>anova(nod.h11, nod.h11.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b3 ~ treat + b.satisfaction.b3 + subcounty.blocking
## Model 2: e.satisfaction.b3 ~ treat + S3 + b.satisfaction.b3 + subcounty.blocking
##   Res.Df  RSS Df Sum of Sq      F  Pr(&gt;F)  
## 1    980 1144                              
## 2    975 1133  5    10.958 1.8861 0.09416 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>anova(nod.h10mgmt, nod.h10mgmt.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.satisfaction.b2 ~ treat + b.satisfaction.b2 + subcounty.blocking
## Model 2: e.satisfaction.b2 ~ treat + S3 + b.satisfaction.b2 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    984 959.12                           
## 2    979 956.34  5    2.7757 0.5683 0.7244</code></pre>
<pre class="r"><code>anova(mod.h9.2, mod.h9.2.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b12 ~ treat + b.participate.b12 + subcounty.blocking
## Model 2: e.participate.b12 ~ treat + S3 + b.participate.b12 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    971 572.80                           
## 2    966 570.15  5    2.6473 0.8971 0.4823</code></pre>
<pre class="r"><code>anova(mod.h9.1, mod.h9.1.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b11 ~ treat + b.participate.b11 + subcounty.blocking
## Model 2: e.participate.b11 ~ treat + S3 + b.participate.b11 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F  Pr(&gt;F)  
## 1    980 1140.1                              
## 2    975 1128.3  5    11.803 2.0398 0.07078 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>anova(mod.h8.3, mod.h8.3.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.e4 ~ treat + subcounty.blocking
## Model 2: e.participate.e4 ~ treat + S3 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    988 189.90                           
## 2    983 188.34  5    1.5681 1.6369 0.1475</code></pre>
<pre class="r"><code>anova(mod.h8.1, mod.h8.1.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.participate.b10 ~ treat + b.participate.b10 + subcounty.blocking
## Model 2: e.participate.b10 ~ treat + S3 + b.participate.b10 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    982 823.78                           
## 2    977 821.62  5    2.1585 0.5133 0.7663</code></pre>
<pre class="r"><code>anova(mod.h7.3, mod.h7.3.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b9 ~ treat + b.knowledge.b9 + subcounty.blocking
## Model 2: e.knowledge.b9 ~ treat + S3 + b.knowledge.b9 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    979 440.21                           
## 2    974 437.25  5    2.9555 1.3167 0.2545</code></pre>
<pre class="r"><code>anova(mod.h7.2, mod.h7.2.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b8 ~ treat + b.knowledge.b8 + subcounty.blocking
## Model 2: e.knowledge.b8 ~ treat + S3 + b.knowledge.b8 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    974 508.43                           
## 2    969 506.31  5     2.118 0.8107 0.5421</code></pre>
<pre class="r"><code>anova(mod.h7, mod.h7.spill3)</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: e.knowledge.b7 ~ treat + b.knowledge.b7 + subcounty.blocking
## Model 2: e.knowledge.b7 ~ treat + S3 + b.knowledge.b7 + subcounty.blocking
##   Res.Df    RSS Df Sum of Sq      F Pr(&gt;F)
## 1    985 829.96                           
## 2    980 826.43  5    3.5313 0.8375 0.5231</code></pre>
<pre class="r"><code>######################################################################################
###Figures D1-3. Results for F-Test of Improved Model Fit with Spillover Indicators
#Note: models presented in this section exclude most covariates
######################################################################################

#Figure D1: modifying only models where 2-village spillover indicator improves model fit (custom style)
names &lt;- c(&quot;Satisfied with Information from UWA about RS&quot;,&quot;Know How RS Works Generally&quot;,&quot;Know How RS Works in my Village&quot;,&quot;Opportunities to Participate in RS&quot;,&quot;Participated in RS meeting (past month)&quot;,&quot;Opportunities to Communicate with UWA about RS&quot;,&quot;Know Right Person to Contact about RS&quot;,&quot;Satisfied with Park Management&quot;,&quot;Satisfied with Revenue Sharing&quot;,&quot;Importance of Protecting Bwindi&quot;)
ates &lt;- c(mod.h7$coef[2],mod.h7.2$coef[2],mod.h7.3$coef[2],mod.h8.1$coef[2],mod.h8.3.spill$coef[2],mod.h9.1$coef[2],mod.h9.2$coef[2],nod.h10mgmt$coef[2],nod.h11.spill$coef[2],nod.h13$coef[2])
ses &lt;- c(coef(summary(mod.h7))[2,2],coef(summary(mod.h7.2))[2,2],coef(summary(mod.h7.3))[2,2],coef(summary(mod.h8.1))[2,2],coef(summary(mod.h8.3.spill))[2,2],coef(summary(mod.h9.1))[2,2],coef(summary(mod.h9.2))[2,2],coef(summary(nod.h10mgmt))[2,2],coef(summary(nod.h11.spill))[2,2],coef(summary(nod.h13))[2,2])
dta.coef &lt;- data.frame(names,ates,ses)
dta.coef$ylo &lt;- dta.coef$ates - 1.645*dta.coef$ses
dta.coef$yhi &lt;- dta.coef$ates + 1.645*dta.coef$ses
dta.coef$names &lt;- factor(dta.coef$names, levels=dta.coef$names)

spill = ggplot(dta.coef, aes(x=names, y=ates, ymin=ylo, ymax=yhi)) + geom_errorbar(colour=&quot;blue&quot;, width=0.2, size=1.4) + 
  theme_bw(base_size=16) + coord_flip() + geom_hline(aes(yintercept=0), lty=2) + ylab(&quot;Treatment Effects on Survey Items&quot;) + xlab(&quot;&quot;) + geom_point(size=4, colour=&quot;blue&quot;) +
  theme(axis.title = element_text(size=14))
spill </code></pre>
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" width="624" /></p>
<pre class="r"><code>#Figure D2: modifying only models where 4-village spillover indicator improves model fit (custom style)
names &lt;- c(&quot;Satisfied with Information from UWA about RS&quot;,&quot;Know How RS Works Generally&quot;,&quot;Know How RS Works in my Village&quot;,&quot;Opportunities to Participate in RS&quot;,&quot;Participated in RS meeting (past month)&quot;,&quot;Opportunities to Communicate with UWA about RS&quot;,&quot;Know Right Person to Contact about RS&quot;,&quot;Satisfied with Park Management&quot;,&quot;Satisfied with Revenue Sharing&quot;,&quot;Importance of Protecting Bwindi&quot;)
ates &lt;- c(mod.h7$coef[2],mod.h7.2$coef[2],mod.h7.3$coef[2],mod.h8.1$coef[2],mod.h8.3$coef[2],mod.h9.1$coef[2],mod.h9.2$coef[2],nod.h10mgmt$coef[2],nod.h11.spill2$coef[2],nod.h13$coef[2])
ses &lt;- c(coef(summary(mod.h7))[2,2],coef(summary(mod.h7.2))[2,2],coef(summary(mod.h7.3))[2,2],coef(summary(mod.h8.1))[2,2],coef(summary(mod.h8.3))[2,2],coef(summary(mod.h9.1))[2,2],coef(summary(mod.h9.2))[2,2],coef(summary(nod.h10mgmt))[2,2],coef(summary(nod.h11.spill2))[2,2],coef(summary(nod.h13))[2,2])
dta.coef &lt;- data.frame(names,ates,ses)
dta.coef$ylo &lt;- dta.coef$ates - 1.645*dta.coef$ses
dta.coef$yhi &lt;- dta.coef$ates + 1.645*dta.coef$ses
dta.coef$names &lt;- factor(dta.coef$names, levels=dta.coef$names)

spill2 = ggplot(dta.coef, aes(x=names, y=ates, ymin=ylo, ymax=yhi)) + geom_errorbar(colour=&quot;blue&quot;, width=0.2, size=1.4) + 
  theme_bw(base_size=16) + coord_flip() + geom_hline(aes(yintercept=0), lty=2) + ylab(&quot;Treatment Effects on Survey Items&quot;) + xlab(&quot;&quot;) + geom_point(size=4, colour=&quot;blue&quot;) +
  theme(axis.title = element_text(size=14))
spill2 #Copied at 800x420</code></pre>
<p><img 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" width="624" /></p>
<pre class="r"><code>#Figure D3: modifying only models where 6-village spillover indicator improves model fit (custom style)
names &lt;- c(&quot;Satisfied with Information from UWA about RS&quot;,&quot;Know How RS Works Generally&quot;,&quot;Know How RS Works in my Village&quot;,&quot;Opportunities to Participate in RS&quot;,&quot;Participated in RS meeting (past month)&quot;,&quot;Opportunities to Communicate with UWA about RS&quot;,&quot;Know Right Person to Contact about RS&quot;,&quot;Satisfied with Park Management&quot;,&quot;Satisfied with Revenue Sharing&quot;,&quot;Importance of Protecting Bwindi&quot;)
ates &lt;- c(mod.h7$coef[2],mod.h7.2$coef[2],mod.h7.3$coef[2],mod.h8.1$coef[2],mod.h8.3$coef[2],mod.h9.1.spill3$coef[2],mod.h9.2$coef[2],nod.h10mgmt$coef[2],nod.h11.spill3$coef[2],nod.h13$coef[2])
ses &lt;- c(coef(summary(mod.h7))[2,2],coef(summary(mod.h7.2))[2,2],coef(summary(mod.h7.3))[2,2],coef(summary(mod.h8.1))[2,2],coef(summary(mod.h8.3))[2,2],coef(summary(mod.h9.1.spill3))[2,2],coef(summary(mod.h9.2))[2,2],coef(summary(nod.h10mgmt))[2,2],coef(summary(nod.h11.spill3))[2,2],coef(summary(nod.h13))[2,2])
dta.coef &lt;- data.frame(names,ates,ses)
dta.coef$ylo &lt;- dta.coef$ates - 1.645*dta.coef$ses
dta.coef$yhi &lt;- dta.coef$ates + 1.645*dta.coef$ses
dta.coef$names &lt;- factor(dta.coef$names, levels=dta.coef$names)

spill3 = ggplot(dta.coef, aes(x=names, y=ates, ymin=ylo, ymax=yhi)) + geom_errorbar(colour=&quot;blue&quot;, width=0.2, size=1.4) + 
  theme_bw(base_size=16) + coord_flip() + geom_hline(aes(yintercept=0), lty=2) + ylab(&quot;Treatment Effects on Survey Items&quot;) + xlab(&quot;&quot;) + geom_point(size=4, colour=&quot;blue&quot;) +
  theme(axis.title = element_text(size=14))
spill3 #Copied at 800x420</code></pre>
<p><img 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" width="624" /></p>
<pre class="r"><code>######################################################################################
###Table D4. Treatment effects under the base specification and adjusted for spillover
######################################################################################
#Note: this block produces the values for table; formatting done separately
source('ols.R') #Function to compute OLS w/ clustered SEs

###H7.2: Know How RS Works Generally
mod.h7.2 &lt;- ols(e.knowledge.b8 ~ treat + b.knowledge.b8 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h7.2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.497      0.202  12.337
## treat                                     0.018      0.052   0.354
## b.knowledge.b8                            0.141      0.024   5.974
## E_subject_s_gendermale                    0.232      0.045   5.103
## E_Subject_s_age                          -0.002      0.002  -1.038
## E_Subject_s_income100_000___200_         -0.080      0.134  -0.599
## E_Subject_s_income20_000___100_0         -0.273      0.123  -2.220
## E_Subject_s_income20_000_shillin         -0.377      0.127  -2.971
## E_Subject_s_income200_000___500_         -0.118      0.147  -0.803
## E_Subject_s_income500_000___1_mi          0.049      0.138   0.355
## E_Subject_s_incomerefused_to_ans         -0.667      0.521  -1.282
## E_Can_you_read_001no__i_cannot_r         -0.068      0.263  -0.258
## E_Can_you_read_001yes__i_can_rea          0.372      0.101   3.693
## E_Can_you_read_001yes__i_can_som          0.270      0.104   2.582
## subjects.per.village                      0.002      0.002   0.742
## subcounty.blockingKanungu Town Council    0.368      0.030  12.281
## subcounty.blockingKayonza                 0.190      0.023   8.398
## subcounty.blockingKinaaba                 0.211      0.061   3.485
## subcounty.blockingKirima                  0.162      0.021   7.802
## subcounty.blockingKirundo                 0.034      0.032   1.060
## subcounty.blockingMpungu                  0.234      0.026   9.155
## subcounty.blockingNyabwishenya            0.185      0.023   7.991
## subcounty.blockingRuhija                  0.214      0.020  10.535
## subcounty.blockingSingles                 0.146      0.040   3.682
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.723
## b.knowledge.b8                            0.000
## E_subject_s_gendermale                    0.000
## E_Subject_s_age                           0.299
## E_Subject_s_income100_000___200_          0.549
## E_Subject_s_income20_000___100_0          0.026
## E_Subject_s_income20_000_shillin          0.003
## E_Subject_s_income200_000___500_          0.422
## E_Subject_s_income500_000___1_mi          0.723
## E_Subject_s_incomerefused_to_ans          0.200
## E_Can_you_read_001no__i_cannot_r          0.796
## E_Can_you_read_001yes__i_can_rea          0.000
## E_Can_you_read_001yes__i_can_som          0.010
## subjects.per.village                      0.458
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.289
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h7.2.spill &lt;- ols(e.knowledge.b8 ~ treat + S1 + b.knowledge.b8  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h7.2.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.525      0.251  10.073
## treat                                     0.012      0.058   0.206
## S11                                      -0.049      0.089  -0.545
## S12                                      -0.014      0.088  -0.165
## b.knowledge.b8                            0.140      0.023   6.164
## E_subject_s_gendermale                    0.229      0.048   4.813
## E_Subject_s_age                          -0.002      0.002  -1.015
## E_Subject_s_income100_000___200_         -0.065      0.126  -0.514
## E_Subject_s_income20_000___100_0         -0.257      0.122  -2.116
## E_Subject_s_income20_000_shillin         -0.365      0.131  -2.793
## E_Subject_s_income200_000___500_         -0.101      0.146  -0.692
## E_Subject_s_income500_000___1_mi          0.057      0.135   0.420
## E_Subject_s_incomerefused_to_ans         -0.663      0.516  -1.285
## E_Can_you_read_001no__i_cannot_r         -0.071      0.269  -0.264
## E_Can_you_read_001yes__i_can_rea          0.373      0.100   3.744
## E_Can_you_read_001yes__i_can_som          0.270      0.104   2.604
## subjects.per.village                      0.002      0.002   0.949
## subcounty.blockingKanungu Town Council    0.356      0.043   8.225
## subcounty.blockingKayonza                 0.187      0.024   7.702
## subcounty.blockingKinaaba                 0.184      0.062   2.963
## subcounty.blockingKirima                  0.147      0.032   4.638
## subcounty.blockingKirundo                 0.024      0.031   0.786
## subcounty.blockingMpungu                  0.228      0.023   9.866
## subcounty.blockingNyabwishenya            0.197      0.028   7.020
## subcounty.blockingRuhija                  0.202      0.024   8.273
## subcounty.blockingSingles                 0.127      0.049   2.570
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.837
## S11                                       0.586
## S12                                       0.869
## b.knowledge.b8                            0.000
## E_subject_s_gendermale                    0.000
## E_Subject_s_age                           0.310
## E_Subject_s_income100_000___200_          0.607
## E_Subject_s_income20_000___100_0          0.034
## E_Subject_s_income20_000_shillin          0.005
## E_Subject_s_income200_000___500_          0.489
## E_Subject_s_income500_000___1_mi          0.675
## E_Subject_s_incomerefused_to_ans          0.199
## E_Can_you_read_001no__i_cannot_r          0.792
## E_Can_you_read_001yes__i_can_rea          0.000
## E_Can_you_read_001yes__i_can_som          0.009
## subjects.per.village                      0.343
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.003
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.432
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.010</code></pre>
<pre class="r"><code>mod.h7.2.spill2 &lt;- ols(e.knowledge.b8 ~ treat + S2 + b.knowledge.b8  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h7.2.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.763      0.227  12.166
## treat                                     0.004      0.060   0.066
## S21                                      -0.262      0.118  -2.214
## S22                                      -0.279      0.109  -2.567
## S23                                      -0.292      0.102  -2.875
## S24                                      -0.288      0.114  -2.534
## b.knowledge.b8                            0.138      0.025   5.521
## E_subject_s_gendermale                    0.227      0.045   5.096
## E_Subject_s_age                          -0.002      0.002  -0.981
## E_Subject_s_income100_000___200_         -0.080      0.136  -0.591
## E_Subject_s_income20_000___100_0         -0.274      0.124  -2.214
## E_Subject_s_income20_000_shillin         -0.375      0.127  -2.953
## E_Subject_s_income200_000___500_         -0.118      0.145  -0.816
## E_Subject_s_income500_000___1_mi          0.031      0.132   0.239
## E_Subject_s_incomerefused_to_ans         -0.669      0.517  -1.292
## E_Can_you_read_001no__i_cannot_r         -0.063      0.268  -0.233
## E_Can_you_read_001yes__i_can_rea          0.376      0.105   3.577
## E_Can_you_read_001yes__i_can_som          0.271      0.107   2.535
## subjects.per.village                      0.002      0.002   1.114
## subcounty.blockingKanungu Town Council    0.393      0.058   6.824
## subcounty.blockingKayonza                 0.191      0.025   7.522
## subcounty.blockingKinaaba                 0.200      0.067   2.970
## subcounty.blockingKirima                  0.177      0.013  13.790
## subcounty.blockingKirundo                 0.047      0.026   1.808
## subcounty.blockingMpungu                  0.238      0.027   8.821
## subcounty.blockingNyabwishenya            0.202      0.022   9.146
## subcounty.blockingRuhija                  0.222      0.017  12.791
## subcounty.blockingSingles                 0.104      0.046   2.260
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.947
## S21                                       0.027
## S22                                       0.010
## S23                                       0.004
## S24                                       0.011
## b.knowledge.b8                            0.000
## E_subject_s_gendermale                    0.000
## E_Subject_s_age                           0.327
## E_Subject_s_income100_000___200_          0.555
## E_Subject_s_income20_000___100_0          0.027
## E_Subject_s_income20_000_shillin          0.003
## E_Subject_s_income200_000___500_          0.414
## E_Subject_s_income500_000___1_mi          0.811
## E_Subject_s_incomerefused_to_ans          0.196
## E_Can_you_read_001no__i_cannot_r          0.816
## E_Can_you_read_001yes__i_can_rea          0.000
## E_Can_you_read_001yes__i_can_som          0.011
## subjects.per.village                      0.265
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.003
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.071
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.024</code></pre>
<pre class="r"><code>mod.h7.2.spill3 &lt;- ols(e.knowledge.b8 ~ treat + S3 + b.knowledge.b8  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h7.2.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.739      0.289   9.484
## treat                                     0.008      0.067   0.122
## S32                                      -0.202      0.145  -1.390
## S33                                      -0.233      0.130  -1.787
## S34                                      -0.223      0.151  -1.477
## S35                                      -0.199      0.133  -1.496
## S36                                      -0.215      0.153  -1.406
## b.knowledge.b8                            0.136      0.026   5.187
## E_subject_s_gendermale                    0.231      0.048   4.785
## E_Subject_s_age                          -0.002      0.002  -0.970
## E_Subject_s_income100_000___200_         -0.082      0.131  -0.625
## E_Subject_s_income20_000___100_0         -0.277      0.123  -2.260
## E_Subject_s_income20_000_shillin         -0.374      0.129  -2.906
## E_Subject_s_income200_000___500_         -0.123      0.144  -0.855
## E_Subject_s_income500_000___1_mi          0.035      0.135   0.262
## E_Subject_s_incomerefused_to_ans         -0.672      0.528  -1.272
## E_Can_you_read_001no__i_cannot_r         -0.063      0.268  -0.233
## E_Can_you_read_001yes__i_can_rea          0.374      0.103   3.639
## E_Can_you_read_001yes__i_can_som          0.271      0.106   2.548
## subjects.per.village                      0.002      0.003   0.716
## subcounty.blockingKanungu Town Council    0.365      0.059   6.167
## subcounty.blockingKayonza                 0.184      0.029   6.371
## subcounty.blockingKinaaba                 0.210      0.084   2.511
## subcounty.blockingKirima                  0.158      0.018   9.033
## subcounty.blockingKirundo                 0.027      0.042   0.651
## subcounty.blockingMpungu                  0.213      0.028   7.668
## subcounty.blockingNyabwishenya            0.176      0.035   5.056
## subcounty.blockingRuhija                  0.211      0.021   9.850
## subcounty.blockingSingles                 0.028      0.085   0.330
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.903
## S32                                       0.164
## S33                                       0.074
## S34                                       0.140
## S35                                       0.135
## S36                                       0.160
## b.knowledge.b8                            0.000
## E_subject_s_gendermale                    0.000
## E_Subject_s_age                           0.332
## E_Subject_s_income100_000___200_          0.532
## E_Subject_s_income20_000___100_0          0.024
## E_Subject_s_income20_000_shillin          0.004
## E_Subject_s_income200_000___500_          0.392
## E_Subject_s_income500_000___1_mi          0.793
## E_Subject_s_incomerefused_to_ans          0.203
## E_Can_you_read_001no__i_cannot_r          0.815
## E_Can_you_read_001yes__i_can_rea          0.000
## E_Can_you_read_001yes__i_can_som          0.011
## subjects.per.village                      0.474
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.012
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.515
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.741</code></pre>
<pre class="r"><code>###H7.3: How RS Works in my Village
mod.h7.3 &lt;- ols(e.knowledge.b9 ~ treat + b.knowledge.b9 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.970      0.245  12.105
## treat                                    -0.034      0.038  -0.885
## b.knowledge.b9                            0.150      0.047   3.182
## E_subject_s_gendermale                    0.061      0.042   1.435
## E_Subject_s_age                          -0.001      0.002  -0.460
## E_Subject_s_income100_000___200_         -0.150      0.093  -1.616
## E_Subject_s_income20_000___100_0         -0.193      0.100  -1.920
## E_Subject_s_income20_000_shillin         -0.366      0.119  -3.077
## E_Subject_s_income200_000___500_         -0.183      0.112  -1.636
## E_Subject_s_income500_000___1_mi         -0.069      0.129  -0.538
## E_Subject_s_incomerefused_to_ans         -0.744      0.395  -1.881
## E_Can_you_read_001no__i_cannot_r         -0.280      0.176  -1.588
## E_Can_you_read_001yes__i_can_rea          0.222      0.146   1.521
## E_Can_you_read_001yes__i_can_som          0.173      0.156   1.111
## subjects.per.village                     -0.001      0.002  -0.874
## subcounty.blockingKanungu Town Council    0.193      0.012  15.766
## subcounty.blockingKayonza                 0.042      0.021   2.053
## subcounty.blockingKinaaba                 0.309      0.029  10.742
## subcounty.blockingKirima                  0.132      0.014   9.499
## subcounty.blockingKirundo                 0.061      0.018   3.444
## subcounty.blockingMpungu                  0.153      0.011  13.911
## subcounty.blockingNyabwishenya            0.250      0.012  21.173
## subcounty.blockingRuhija                  0.110      0.012   8.979
## subcounty.blockingSingles                 0.119      0.030   3.910
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.376
## b.knowledge.b9                            0.001
## E_subject_s_gendermale                    0.151
## E_Subject_s_age                           0.646
## E_Subject_s_income100_000___200_          0.106
## E_Subject_s_income20_000___100_0          0.055
## E_Subject_s_income20_000_shillin          0.002
## E_Subject_s_income200_000___500_          0.102
## E_Subject_s_income500_000___1_mi          0.590
## E_Subject_s_incomerefused_to_ans          0.060
## E_Can_you_read_001no__i_cannot_r          0.112
## E_Can_you_read_001yes__i_can_rea          0.128
## E_Can_you_read_001yes__i_can_som          0.266
## subjects.per.village                      0.382
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.040
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.001
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h7.3.spill &lt;- ols(e.knowledge.b9 ~ treat + S1 + b.knowledge.b9  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.3.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.928      0.249  11.771
## treat                                    -0.028      0.043  -0.646
## S11                                       0.011      0.080   0.136
## S12                                       0.087      0.076   1.147
## b.knowledge.b9                            0.148      0.045   3.328
## E_subject_s_gendermale                    0.059      0.046   1.286
## E_Subject_s_age                          -0.001      0.002  -0.437
## E_Subject_s_income100_000___200_         -0.136      0.088  -1.547
## E_Subject_s_income20_000___100_0         -0.185      0.102  -1.808
## E_Subject_s_income20_000_shillin         -0.361      0.124  -2.912
## E_Subject_s_income200_000___500_         -0.173      0.112  -1.541
## E_Subject_s_income500_000___1_mi         -0.062      0.133  -0.461
## E_Subject_s_incomerefused_to_ans         -0.730      0.389  -1.877
## E_Can_you_read_001no__i_cannot_r         -0.275      0.179  -1.536
## E_Can_you_read_001yes__i_can_rea          0.230      0.143   1.611
## E_Can_you_read_001yes__i_can_som          0.181      0.153   1.179
## subjects.per.village                     -0.001      0.001  -0.897
## subcounty.blockingKanungu Town Council    0.153      0.038   4.007
## subcounty.blockingKayonza                 0.042      0.019   2.183
## subcounty.blockingKinaaba                 0.325      0.055   5.899
## subcounty.blockingKirima                  0.110      0.022   5.007
## subcounty.blockingKirundo                 0.063      0.017   3.644
## subcounty.blockingMpungu                  0.158      0.009  17.965
## subcounty.blockingNyabwishenya            0.261      0.028   9.269
## subcounty.blockingRuhija                  0.106      0.017   6.160
## subcounty.blockingSingles                 0.140      0.036   3.860
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.519
## S11                                       0.892
## S12                                       0.251
## b.knowledge.b9                            0.001
## E_subject_s_gendermale                    0.198
## E_Subject_s_age                           0.662
## E_Subject_s_income100_000___200_          0.122
## E_Subject_s_income20_000___100_0          0.071
## E_Subject_s_income20_000_shillin          0.004
## E_Subject_s_income200_000___500_          0.123
## E_Subject_s_income500_000___1_mi          0.645
## E_Subject_s_incomerefused_to_ans          0.060
## E_Can_you_read_001no__i_cannot_r          0.125
## E_Can_you_read_001yes__i_can_rea          0.107
## E_Can_you_read_001yes__i_can_som          0.238
## subjects.per.village                      0.370
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.029
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h7.3.spill2 &lt;- ols(e.knowledge.b9 ~ treat + S2 + b.knowledge.b9  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.3.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.024      0.361   8.387
## treat                                    -0.047      0.046  -1.006
## S21                                      -0.014      0.184  -0.076
## S22                                      -0.021      0.197  -0.106
## S23                                      -0.065      0.168  -0.385
## S24                                      -0.190      0.216  -0.879
## b.knowledge.b9                            0.150      0.047   3.211
## E_subject_s_gendermale                    0.063      0.042   1.489
## E_Subject_s_age                          -0.001      0.002  -0.486
## E_Subject_s_income100_000___200_         -0.151      0.100  -1.517
## E_Subject_s_income20_000___100_0         -0.192      0.106  -1.816
## E_Subject_s_income20_000_shillin         -0.364      0.121  -2.999
## E_Subject_s_income200_000___500_         -0.181      0.119  -1.515
## E_Subject_s_income500_000___1_mi         -0.056      0.125  -0.449
## E_Subject_s_incomerefused_to_ans         -0.744      0.398  -1.869
## E_Can_you_read_001no__i_cannot_r         -0.286      0.179  -1.599
## E_Can_you_read_001yes__i_can_rea          0.214      0.143   1.503
## E_Can_you_read_001yes__i_can_som          0.164      0.153   1.067
## subjects.per.village                     -0.001      0.002  -0.696
## subcounty.blockingKanungu Town Council    0.252      0.046   5.530
## subcounty.blockingKayonza                 0.029      0.033   0.866
## subcounty.blockingKinaaba                 0.274      0.050   5.448
## subcounty.blockingKirima                  0.127      0.017   7.354
## subcounty.blockingKirundo                 0.041      0.025   1.619
## subcounty.blockingMpungu                  0.134      0.022   5.976
## subcounty.blockingNyabwishenya            0.245      0.020  12.371
## subcounty.blockingRuhija                  0.089      0.028   3.189
## subcounty.blockingSingles                 0.089      0.051   1.745
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.314
## S21                                       0.939
## S22                                       0.915
## S23                                       0.700
## S24                                       0.379
## b.knowledge.b9                            0.001
## E_subject_s_gendermale                    0.136
## E_Subject_s_age                           0.627
## E_Subject_s_income100_000___200_          0.129
## E_Subject_s_income20_000___100_0          0.069
## E_Subject_s_income20_000_shillin          0.003
## E_Subject_s_income200_000___500_          0.130
## E_Subject_s_income500_000___1_mi          0.653
## E_Subject_s_incomerefused_to_ans          0.062
## E_Can_you_read_001no__i_cannot_r          0.110
## E_Can_you_read_001yes__i_can_rea          0.133
## E_Can_you_read_001yes__i_can_som          0.286
## subjects.per.village                      0.487
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.387
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.106
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.001
## subcounty.blockingSingles                 0.081</code></pre>
<pre class="r"><code>mod.h7.3.spill3 &lt;- ols(e.knowledge.b9 ~ treat + S3 + b.knowledge.b9  + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.3.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.201      0.228  14.045
## treat                                    -0.041      0.050  -0.829
## S32                                      -0.172      0.050  -3.421
## S33                                      -0.219      0.044  -5.015
## S34                                      -0.157      0.093  -1.690
## S35                                      -0.158      0.084  -1.877
## S36                                      -0.401      0.072  -5.541
## b.knowledge.b9                            0.150      0.046   3.235
## E_subject_s_gendermale                    0.055      0.046   1.196
## E_Subject_s_age                          -0.001      0.002  -0.425
## E_Subject_s_income100_000___200_         -0.165      0.091  -1.808
## E_Subject_s_income20_000___100_0         -0.209      0.101  -2.056
## E_Subject_s_income20_000_shillin         -0.376      0.118  -3.178
## E_Subject_s_income200_000___500_         -0.199      0.113  -1.768
## E_Subject_s_income500_000___1_mi         -0.073      0.127  -0.575
## E_Subject_s_incomerefused_to_ans         -0.763      0.395  -1.933
## E_Can_you_read_001no__i_cannot_r         -0.276      0.180  -1.535
## E_Can_you_read_001yes__i_can_rea          0.222      0.135   1.643
## E_Can_you_read_001yes__i_can_som          0.169      0.146   1.156
## subjects.per.village                     -0.002      0.002  -0.730
## subcounty.blockingKanungu Town Council    0.142      0.056   2.541
## subcounty.blockingKayonza                 0.021      0.031   0.695
## subcounty.blockingKinaaba                 0.316      0.066   4.796
## subcounty.blockingKirima                  0.112      0.015   7.256
## subcounty.blockingKirundo                 0.037      0.039   0.958
## subcounty.blockingMpungu                  0.119      0.014   8.804
## subcounty.blockingNyabwishenya            0.210      0.027   7.906
## subcounty.blockingRuhija                  0.095      0.022   4.344
## subcounty.blockingSingles                -0.002      0.054  -0.045
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.407
## S32                                       0.001
## S33                                       0.000
## S34                                       0.091
## S35                                       0.061
## S36                                       0.000
## b.knowledge.b9                            0.001
## E_subject_s_gendermale                    0.232
## E_Subject_s_age                           0.671
## E_Subject_s_income100_000___200_          0.071
## E_Subject_s_income20_000___100_0          0.040
## E_Subject_s_income20_000_shillin          0.001
## E_Subject_s_income200_000___500_          0.077
## E_Subject_s_income500_000___1_mi          0.565
## E_Subject_s_incomerefused_to_ans          0.053
## E_Can_you_read_001no__i_cannot_r          0.125
## E_Can_you_read_001yes__i_can_rea          0.100
## E_Can_you_read_001yes__i_can_som          0.247
## subjects.per.village                      0.466
## subcounty.blockingKanungu Town Council    0.011
## subcounty.blockingKayonza                 0.487
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.338
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.964</code></pre>
<pre class="r"><code>###H9.2: Know Right Person to Contact
mod.h9.2 &lt;- ols(e.participate.b12 ~ treat + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h9.2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.070      0.215  14.270
## treat                                     0.002      0.043   0.052
## b.participate.b12                         0.128      0.032   3.975
## E_subject_s_gendermale                    0.129      0.044   2.966
## E_Subject_s_age                           0.003      0.002   1.335
## E_Subject_s_income100_000___200_         -0.081      0.113  -0.717
## E_Subject_s_income20_000___100_0         -0.073      0.138  -0.530
## E_Subject_s_income20_000_shillin         -0.148      0.136  -1.093
## E_Subject_s_income200_000___500_         -0.060      0.136  -0.441
## E_Subject_s_income500_000___1_mi          0.071      0.127   0.555
## E_Subject_s_incomerefused_to_ans         -0.356      0.431  -0.825
## E_Can_you_read_001no__i_cannot_r         -0.143      0.124  -1.159
## E_Can_you_read_001yes__i_can_rea          0.092      0.056   1.658
## E_Can_you_read_001yes__i_can_som          0.090      0.036   2.478
## subjects.per.village                     -0.005      0.002  -2.772
## subcounty.blockingKanungu Town Council    0.196      0.030   6.447
## subcounty.blockingKayonza                -0.124      0.019  -6.532
## subcounty.blockingKinaaba                 0.158      0.032   4.862
## subcounty.blockingKirima                 -0.042      0.015  -2.762
## subcounty.blockingKirundo                 0.093      0.025   3.650
## subcounty.blockingMpungu                 -0.011      0.023  -0.500
## subcounty.blockingNyabwishenya            0.264      0.016  16.349
## subcounty.blockingRuhija                  0.052      0.011   4.725
## subcounty.blockingSingles                -0.138      0.037  -3.773
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.959
## b.participate.b12                         0.000
## E_subject_s_gendermale                    0.003
## E_Subject_s_age                           0.182
## E_Subject_s_income100_000___200_          0.473
## E_Subject_s_income20_000___100_0          0.596
## E_Subject_s_income20_000_shillin          0.275
## E_Subject_s_income200_000___500_          0.659
## E_Subject_s_income500_000___1_mi          0.579
## E_Subject_s_incomerefused_to_ans          0.410
## E_Can_you_read_001no__i_cannot_r          0.247
## E_Can_you_read_001yes__i_can_rea          0.097
## E_Can_you_read_001yes__i_can_som          0.013
## subjects.per.village                      0.006
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.006
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.617
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>#To Do: the SI entries in this cell are incorrect, fix

mod.h9.2.spill &lt;- ols(e.participate.b12 ~ treat + S1 + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.2.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.091      0.202  15.331
## treat                                     0.000      0.050  -0.004
## S11                                       0.003      0.058   0.060
## S12                                      -0.042      0.066  -0.626
## b.participate.b12                         0.128      0.032   3.985
## E_subject_s_gendermale                    0.131      0.044   2.946
## E_Subject_s_age                           0.003      0.003   1.320
## E_Subject_s_income100_000___200_         -0.091      0.111  -0.817
## E_Subject_s_income20_000___100_0         -0.080      0.140  -0.573
## E_Subject_s_income20_000_shillin         -0.154      0.139  -1.105
## E_Subject_s_income200_000___500_         -0.069      0.135  -0.508
## E_Subject_s_income500_000___1_mi          0.065      0.127   0.512
## E_Subject_s_incomerefused_to_ans         -0.364      0.439  -0.829
## E_Can_you_read_001no__i_cannot_r         -0.145      0.127  -1.138
## E_Can_you_read_001yes__i_can_rea          0.088      0.057   1.536
## E_Can_you_read_001yes__i_can_som          0.086      0.037   2.308
## subjects.per.village                     -0.005      0.002  -2.836
## subcounty.blockingKanungu Town Council    0.218      0.028   7.839
## subcounty.blockingKayonza                -0.123      0.015  -8.369
## subcounty.blockingKinaaba                 0.155      0.066   2.350
## subcounty.blockingKirima                 -0.027      0.017  -1.629
## subcounty.blockingKirundo                 0.094      0.020   4.786
## subcounty.blockingMpungu                 -0.013      0.015  -0.905
## subcounty.blockingNyabwishenya            0.256      0.025  10.304
## subcounty.blockingRuhija                  0.057      0.015   3.752
## subcounty.blockingSingles                -0.145      0.023  -6.329
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.997
## S11                                       0.952
## S12                                       0.531
## b.participate.b12                         0.000
## E_subject_s_gendermale                    0.003
## E_Subject_s_age                           0.187
## E_Subject_s_income100_000___200_          0.414
## E_Subject_s_income20_000___100_0          0.567
## E_Subject_s_income20_000_shillin          0.269
## E_Subject_s_income200_000___500_          0.611
## E_Subject_s_income500_000___1_mi          0.609
## E_Subject_s_incomerefused_to_ans          0.407
## E_Can_you_read_001no__i_cannot_r          0.255
## E_Can_you_read_001yes__i_can_rea          0.124
## E_Can_you_read_001yes__i_can_som          0.021
## subjects.per.village                      0.005
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.019
## subcounty.blockingKirima                  0.103
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.366
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h9.2.spill2 &lt;- ols(e.participate.b12 ~ treat + S2 + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.2.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.303      0.223  14.828
## treat                                    -0.012      0.052  -0.236
## S21                                      -0.185      0.040  -4.675
## S22                                      -0.270      0.080  -3.381
## S23                                      -0.264      0.059  -4.479
## S24                                      -0.220      0.104  -2.124
## b.participate.b12                         0.125      0.030   4.199
## E_subject_s_gendermale                    0.124      0.042   2.943
## E_Subject_s_age                           0.004      0.002   1.449
## E_Subject_s_income100_000___200_         -0.073      0.110  -0.662
## E_Subject_s_income20_000___100_0         -0.066      0.137  -0.479
## E_Subject_s_income20_000_shillin         -0.138      0.136  -1.015
## E_Subject_s_income200_000___500_         -0.052      0.134  -0.391
## E_Subject_s_income500_000___1_mi          0.059      0.119   0.499
## E_Subject_s_incomerefused_to_ans         -0.347      0.445  -0.778
## E_Can_you_read_001no__i_cannot_r         -0.143      0.125  -1.148
## E_Can_you_read_001yes__i_can_rea          0.093      0.055   1.701
## E_Can_you_read_001yes__i_can_som          0.088      0.035   2.491
## subjects.per.village                     -0.004      0.002  -1.934
## subcounty.blockingKanungu Town Council    0.206      0.036   5.760
## subcounty.blockingKayonza                -0.134      0.018  -7.656
## subcounty.blockingKinaaba                 0.137      0.050   2.771
## subcounty.blockingKirima                 -0.033      0.016  -2.023
## subcounty.blockingKirundo                 0.110      0.034   3.261
## subcounty.blockingMpungu                 -0.025      0.027  -0.927
## subcounty.blockingNyabwishenya            0.281      0.021  13.223
## subcounty.blockingRuhija                  0.060      0.021   2.838
## subcounty.blockingSingles                -0.201      0.028  -7.085
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.814
## S21                                       0.000
## S22                                       0.001
## S23                                       0.000
## S24                                       0.034
## b.participate.b12                         0.000
## E_subject_s_gendermale                    0.003
## E_Subject_s_age                           0.147
## E_Subject_s_income100_000___200_          0.508
## E_Subject_s_income20_000___100_0          0.632
## E_Subject_s_income20_000_shillin          0.310
## E_Subject_s_income200_000___500_          0.696
## E_Subject_s_income500_000___1_mi          0.618
## E_Subject_s_incomerefused_to_ans          0.436
## E_Can_you_read_001no__i_cannot_r          0.251
## E_Can_you_read_001yes__i_can_rea          0.089
## E_Can_you_read_001yes__i_can_som          0.013
## subjects.per.village                      0.053
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.006
## subcounty.blockingKirima                  0.043
## subcounty.blockingKirundo                 0.001
## subcounty.blockingMpungu                  0.354
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.005
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h9.2.spill3 &lt;- ols(e.participate.b12 ~ treat + S3 + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.2.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.266      0.202  16.152
## treat                                    -0.029      0.055  -0.523
## S32                                      -0.114      0.135  -0.842
## S33                                      -0.132      0.161  -0.817
## S34                                      -0.199      0.157  -1.263
## S35                                      -0.125      0.172  -0.729
## S36                                      -0.345      0.199  -1.730
## b.participate.b12                         0.127      0.032   4.008
## E_subject_s_gendermale                    0.133      0.044   3.049
## E_Subject_s_age                           0.003      0.003   1.257
## E_Subject_s_income100_000___200_         -0.091      0.112  -0.807
## E_Subject_s_income20_000___100_0         -0.083      0.133  -0.623
## E_Subject_s_income20_000_shillin         -0.156      0.142  -1.101
## E_Subject_s_income200_000___500_         -0.068      0.133  -0.516
## E_Subject_s_income500_000___1_mi          0.069      0.120   0.572
## E_Subject_s_incomerefused_to_ans         -0.366      0.439  -0.834
## E_Can_you_read_001no__i_cannot_r         -0.144      0.129  -1.111
## E_Can_you_read_001yes__i_can_rea          0.081      0.054   1.503
## E_Can_you_read_001yes__i_can_som          0.079      0.035   2.268
## subjects.per.village                     -0.005      0.002  -2.173
## subcounty.blockingKanungu Town Council    0.219      0.031   7.091
## subcounty.blockingKayonza                -0.152      0.018  -8.373
## subcounty.blockingKinaaba                 0.101      0.039   2.609
## subcounty.blockingKirima                 -0.067      0.015  -4.368
## subcounty.blockingKirundo                 0.060      0.030   2.019
## subcounty.blockingMpungu                 -0.047      0.019  -2.396
## subcounty.blockingNyabwishenya            0.254      0.029   8.757
## subcounty.blockingRuhija                  0.023      0.018   1.291
## subcounty.blockingSingles                -0.238      0.080  -2.956
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.601
## S32                                       0.400
## S33                                       0.414
## S34                                       0.207
## S35                                       0.466
## S36                                       0.084
## b.participate.b12                         0.000
## E_subject_s_gendermale                    0.002
## E_Subject_s_age                           0.209
## E_Subject_s_income100_000___200_          0.420
## E_Subject_s_income20_000___100_0          0.533
## E_Subject_s_income20_000_shillin          0.271
## E_Subject_s_income200_000___500_          0.606
## E_Subject_s_income500_000___1_mi          0.567
## E_Subject_s_incomerefused_to_ans          0.404
## E_Can_you_read_001no__i_cannot_r          0.266
## E_Can_you_read_001yes__i_can_rea          0.133
## E_Can_you_read_001yes__i_can_som          0.023
## subjects.per.village                      0.030
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.009
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.043
## subcounty.blockingMpungu                  0.017
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.197
## subcounty.blockingSingles                 0.003</code></pre>
<pre class="r"><code>###H9.1: Satisfaction with opportunities to communicate with UWA about RS
mod.h9.1 &lt;- ols(e.participate.b11 ~ treat + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h9.1</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.494      0.344  10.164
## treat                                    -0.021      0.073  -0.287
## b.participate.b11                         0.073      0.028   2.579
## E_subject_s_gendermale                    0.137      0.047   2.913
## E_Subject_s_age                          -0.001      0.001  -0.871
## E_Subject_s_income100_000___200_         -0.063      0.278  -0.226
## E_Subject_s_income20_000___100_0         -0.256      0.305  -0.839
## E_Subject_s_income20_000_shillin         -0.378      0.311  -1.216
## E_Subject_s_income200_000___500_         -0.015      0.290  -0.053
## E_Subject_s_income500_000___1_mi         -0.119      0.330  -0.362
## E_Subject_s_incomerefused_to_ans         -1.731      0.496  -3.486
## E_Can_you_read_001no__i_cannot_r          0.010      0.155   0.065
## E_Can_you_read_001yes__i_can_rea          0.140      0.092   1.517
## E_Can_you_read_001yes__i_can_som          0.246      0.097   2.536
## subjects.per.village                      0.001      0.003   0.299
## subcounty.blockingKanungu Town Council    0.401      0.030  13.442
## subcounty.blockingKayonza                 0.190      0.027   7.019
## subcounty.blockingKinaaba                 0.059      0.085   0.692
## subcounty.blockingKirima                  0.196      0.013  15.302
## subcounty.blockingKirundo                 0.257      0.028   9.013
## subcounty.blockingMpungu                  0.097      0.026   3.671
## subcounty.blockingNyabwishenya            0.678      0.022  30.560
## subcounty.blockingRuhija                  0.049      0.007   7.008
## subcounty.blockingSingles                 0.128      0.038   3.368
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.774
## b.participate.b11                         0.010
## E_subject_s_gendermale                    0.004
## E_Subject_s_age                           0.384
## E_Subject_s_income100_000___200_          0.821
## E_Subject_s_income20_000___100_0          0.401
## E_Subject_s_income20_000_shillin          0.224
## E_Subject_s_income200_000___500_          0.957
## E_Subject_s_income500_000___1_mi          0.718
## E_Subject_s_incomerefused_to_ans          0.000
## E_Can_you_read_001no__i_cannot_r          0.948
## E_Can_you_read_001yes__i_can_rea          0.129
## E_Can_you_read_001yes__i_can_som          0.011
## subjects.per.village                      0.765
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.489
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.001</code></pre>
<pre class="r"><code>mod.h9.1.spill &lt;- ols(e.participate.b11 ~ treat + S1 + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.1.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.530      0.340  10.380
## treat                                    -0.029      0.076  -0.377
## S11                                      -0.042      0.113  -0.368
## S12                                      -0.050      0.126  -0.399
## b.participate.b11                         0.074      0.029   2.589
## E_subject_s_gendermale                    0.136      0.047   2.909
## E_Subject_s_age                          -0.001      0.001  -0.890
## E_Subject_s_income100_000___200_         -0.057      0.286  -0.198
## E_Subject_s_income20_000___100_0         -0.247      0.318  -0.778
## E_Subject_s_income20_000_shillin         -0.370      0.319  -1.160
## E_Subject_s_income200_000___500_         -0.006      0.303  -0.021
## E_Subject_s_income500_000___1_mi         -0.117      0.333  -0.351
## E_Subject_s_incomerefused_to_ans         -1.733      0.496  -3.491
## E_Can_you_read_001no__i_cannot_r          0.005      0.154   0.035
## E_Can_you_read_001yes__i_can_rea          0.137      0.095   1.450
## E_Can_you_read_001yes__i_can_som          0.242      0.100   2.432
## subjects.per.village                      0.001      0.003   0.302
## subcounty.blockingKanungu Town Council    0.410      0.048   8.510
## subcounty.blockingKayonza                 0.188      0.025   7.423
## subcounty.blockingKinaaba                 0.032      0.129   0.247
## subcounty.blockingKirima                  0.194      0.030   6.485
## subcounty.blockingKirundo                 0.248      0.026   9.574
## subcounty.blockingMpungu                  0.091      0.031   2.891
## subcounty.blockingNyabwishenya            0.681      0.035  19.524
## subcounty.blockingRuhija                  0.041      0.027   1.511
## subcounty.blockingSingles                 0.105      0.050   2.109
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.706
## S11                                       0.713
## S12                                       0.690
## b.participate.b11                         0.010
## E_subject_s_gendermale                    0.004
## E_Subject_s_age                           0.374
## E_Subject_s_income100_000___200_          0.843
## E_Subject_s_income20_000___100_0          0.437
## E_Subject_s_income20_000_shillin          0.246
## E_Subject_s_income200_000___500_          0.983
## E_Subject_s_income500_000___1_mi          0.726
## E_Subject_s_incomerefused_to_ans          0.000
## E_Can_you_read_001no__i_cannot_r          0.972
## E_Can_you_read_001yes__i_can_rea          0.147
## E_Can_you_read_001yes__i_can_som          0.015
## subjects.per.village                      0.763
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.805
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.004
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.131
## subcounty.blockingSingles                 0.035</code></pre>
<pre class="r"><code>mod.h9.1.spill2 &lt;- ols(e.participate.b11 ~ treat + S2 + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.1.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.836      0.296  12.965
## treat                                    -0.050      0.076  -0.653
## S21                                      -0.285      0.165  -1.727
## S22                                      -0.315      0.211  -1.489
## S23                                      -0.343      0.178  -1.933
## S24                                      -0.742      0.154  -4.815
## b.participate.b11                         0.077      0.027   2.854
## E_subject_s_gendermale                    0.135      0.046   2.897
## E_Subject_s_age                          -0.001      0.001  -0.832
## E_Subject_s_income100_000___200_         -0.071      0.309  -0.229
## E_Subject_s_income20_000___100_0         -0.264      0.326  -0.810
## E_Subject_s_income20_000_shillin         -0.378      0.336  -1.127
## E_Subject_s_income200_000___500_         -0.018      0.316  -0.057
## E_Subject_s_income500_000___1_mi         -0.106      0.318  -0.334
## E_Subject_s_incomerefused_to_ans         -1.736      0.513  -3.387
## E_Can_you_read_001no__i_cannot_r         -0.004      0.153  -0.028
## E_Can_you_read_001yes__i_can_rea          0.124      0.100   1.230
## E_Can_you_read_001yes__i_can_som          0.222      0.107   2.086
## subjects.per.village                      0.002      0.003   0.559
## subcounty.blockingKanungu Town Council    0.535      0.037  14.370
## subcounty.blockingKayonza                 0.179      0.034   5.228
## subcounty.blockingKinaaba                 0.001      0.075   0.008
## subcounty.blockingKirima                  0.181      0.015  12.339
## subcounty.blockingKirundo                 0.232      0.039   6.009
## subcounty.blockingMpungu                  0.067      0.025   2.703
## subcounty.blockingNyabwishenya            0.667      0.033  20.515
## subcounty.blockingRuhija                  0.021      0.018   1.157
## subcounty.blockingSingles                 0.040      0.032   1.258
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.514
## S21                                       0.084
## S22                                       0.137
## S23                                       0.053
## S24                                       0.000
## b.participate.b11                         0.004
## E_subject_s_gendermale                    0.004
## E_Subject_s_age                           0.405
## E_Subject_s_income100_000___200_          0.819
## E_Subject_s_income20_000___100_0          0.418
## E_Subject_s_income20_000_shillin          0.260
## E_Subject_s_income200_000___500_          0.954
## E_Subject_s_income500_000___1_mi          0.738
## E_Subject_s_incomerefused_to_ans          0.001
## E_Can_you_read_001no__i_cannot_r          0.978
## E_Can_you_read_001yes__i_can_rea          0.219
## E_Can_you_read_001yes__i_can_som          0.037
## subjects.per.village                      0.576
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.993
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.007
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.247
## subcounty.blockingSingles                 0.208</code></pre>
<pre class="r"><code>mod.h9.1.spill3 &lt;- ols(e.participate.b11 ~ treat + S3 + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster = &quot;subcounty.blocking&quot;)
mod.h9.1.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               4.032      0.364  11.061
## treat                                    -0.063      0.072  -0.873
## S32                                      -0.357      0.098  -3.657
## S33                                      -0.427      0.107  -4.005
## S34                                      -0.390      0.163  -2.387
## S35                                      -0.230      0.169  -1.366
## S36                                      -1.043      0.157  -6.663
## b.participate.b11                         0.072      0.028   2.562
## E_subject_s_gendermale                    0.129      0.048   2.661
## E_Subject_s_age                          -0.001      0.001  -0.856
## E_Subject_s_income100_000___200_         -0.106      0.299  -0.353
## E_Subject_s_income20_000___100_0         -0.300      0.323  -0.927
## E_Subject_s_income20_000_shillin         -0.415      0.336  -1.234
## E_Subject_s_income200_000___500_         -0.059      0.309  -0.189
## E_Subject_s_income500_000___1_mi         -0.126      0.318  -0.396
## E_Subject_s_incomerefused_to_ans         -1.772      0.503  -3.524
## E_Can_you_read_001no__i_cannot_r          0.014      0.157   0.090
## E_Can_you_read_001yes__i_can_rea          0.129      0.110   1.175
## E_Can_you_read_001yes__i_can_som          0.227      0.115   1.964
## subjects.per.village                      0.001      0.003   0.293
## subcounty.blockingKanungu Town Council    0.312      0.070   4.426
## subcounty.blockingKayonza                 0.123      0.027   4.494
## subcounty.blockingKinaaba                 0.011      0.108   0.104
## subcounty.blockingKirima                  0.109      0.021   5.284
## subcounty.blockingKirundo                 0.182      0.029   6.330
## subcounty.blockingMpungu                  0.008      0.034   0.247
## subcounty.blockingNyabwishenya            0.602      0.031  19.534
## subcounty.blockingRuhija                 -0.008      0.016  -0.499
## subcounty.blockingSingles                -0.141      0.049  -2.907
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.383
## S32                                       0.000
## S33                                       0.000
## S34                                       0.017
## S35                                       0.172
## S36                                       0.000
## b.participate.b11                         0.010
## E_subject_s_gendermale                    0.008
## E_Subject_s_age                           0.392
## E_Subject_s_income100_000___200_          0.724
## E_Subject_s_income20_000___100_0          0.354
## E_Subject_s_income20_000_shillin          0.217
## E_Subject_s_income200_000___500_          0.850
## E_Subject_s_income500_000___1_mi          0.692
## E_Subject_s_incomerefused_to_ans          0.000
## E_Can_you_read_001no__i_cannot_r          0.928
## E_Can_you_read_001yes__i_can_rea          0.240
## E_Can_you_read_001yes__i_can_som          0.050
## subjects.per.village                      0.769
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.918
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.805
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.618
## subcounty.blockingSingles                 0.004</code></pre>
<pre class="r"><code>###H8.1: Opportunities to Participate in Planning
mod.h8.1 &lt;- ols(e.participate.b10 ~ treat + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.1</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.152      0.439   7.181
## treat                                    -0.122      0.052  -2.374
## b.participate.b10                         0.105      0.035   2.990
## E_subject_s_gendermale                    0.158      0.095   1.668
## E_Subject_s_age                           0.006      0.003   1.791
## E_Subject_s_income100_000___200_         -0.169      0.309  -0.546
## E_Subject_s_income20_000___100_0         -0.151      0.325  -0.464
## E_Subject_s_income20_000_shillin         -0.244      0.339  -0.720
## E_Subject_s_income200_000___500_         -0.189      0.325  -0.581
## E_Subject_s_income500_000___1_mi          0.192      0.343   0.561
## E_Subject_s_incomerefused_to_ans         -0.763      0.497  -1.535
## E_Can_you_read_001no__i_cannot_r         -0.236      0.198  -1.193
## E_Can_you_read_001yes__i_can_rea         -0.092      0.097  -0.949
## E_Can_you_read_001yes__i_can_som         -0.060      0.110  -0.546
## subjects.per.village                     -0.003      0.005  -0.613
## subcounty.blockingKanungu Town Council    0.109      0.057   1.909
## subcounty.blockingKayonza                -0.081      0.044  -1.823
## subcounty.blockingKinaaba                 0.106      0.095   1.119
## subcounty.blockingKirima                 -0.001      0.021  -0.053
## subcounty.blockingKirundo                -0.203      0.056  -3.653
## subcounty.blockingMpungu                  0.082      0.019   4.232
## subcounty.blockingNyabwishenya           -0.095      0.040  -2.388
## subcounty.blockingRuhija                  0.069      0.018   3.767
## subcounty.blockingSingles                -0.044      0.057  -0.762
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.018
## b.participate.b10                         0.003
## E_subject_s_gendermale                    0.095
## E_Subject_s_age                           0.073
## E_Subject_s_income100_000___200_          0.585
## E_Subject_s_income20_000___100_0          0.642
## E_Subject_s_income20_000_shillin          0.471
## E_Subject_s_income200_000___500_          0.561
## E_Subject_s_income500_000___1_mi          0.575
## E_Subject_s_incomerefused_to_ans          0.125
## E_Can_you_read_001no__i_cannot_r          0.233
## E_Can_you_read_001yes__i_can_rea          0.342
## E_Can_you_read_001yes__i_can_som          0.585
## subjects.per.village                      0.540
## subcounty.blockingKanungu Town Council    0.056
## subcounty.blockingKayonza                 0.068
## subcounty.blockingKinaaba                 0.263
## subcounty.blockingKirima                  0.958
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.017
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.446</code></pre>
<pre class="r"><code>mod.h8.1.spill &lt;- ols(e.participate.b10 ~ treat + S1 + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.1.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.135      0.425   7.378
## treat                                    -0.121      0.049  -2.485
## S11                                      -0.031      0.067  -0.473
## S12                                       0.081      0.109   0.744
## b.participate.b10                         0.101      0.034   3.009
## E_subject_s_gendermale                    0.153      0.099   1.549
## E_Subject_s_age                           0.006      0.003   1.823
## E_Subject_s_income100_000___200_         -0.142      0.314  -0.454
## E_Subject_s_income20_000___100_0         -0.130      0.335  -0.387
## E_Subject_s_income20_000_shillin         -0.230      0.344  -0.668
## E_Subject_s_income200_000___500_         -0.164      0.336  -0.489
## E_Subject_s_income500_000___1_mi          0.206      0.353   0.585
## E_Subject_s_incomerefused_to_ans         -0.746      0.500  -1.493
## E_Can_you_read_001no__i_cannot_r         -0.236      0.199  -1.186
## E_Can_you_read_001yes__i_can_rea         -0.083      0.100  -0.828
## E_Can_you_read_001yes__i_can_som         -0.051      0.116  -0.441
## subjects.per.village                     -0.002      0.004  -0.561
## subcounty.blockingKanungu Town Council    0.055      0.071   0.765
## subcounty.blockingKayonza                -0.082      0.039  -2.119
## subcounty.blockingKinaaba                 0.099      0.115   0.856
## subcounty.blockingKirima                 -0.039      0.032  -1.215
## subcounty.blockingKirundo                -0.209      0.045  -4.606
## subcounty.blockingMpungu                  0.082      0.021   3.831
## subcounty.blockingNyabwishenya           -0.072      0.053  -1.361
## subcounty.blockingRuhija                  0.054      0.019   2.937
## subcounty.blockingSingles                -0.037      0.039  -0.939
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.013
## S11                                       0.636
## S12                                       0.457
## b.participate.b10                         0.003
## E_subject_s_gendermale                    0.121
## E_Subject_s_age                           0.068
## E_Subject_s_income100_000___200_          0.650
## E_Subject_s_income20_000___100_0          0.699
## E_Subject_s_income20_000_shillin          0.504
## E_Subject_s_income200_000___500_          0.625
## E_Subject_s_income500_000___1_mi          0.559
## E_Subject_s_incomerefused_to_ans          0.136
## E_Can_you_read_001no__i_cannot_r          0.236
## E_Can_you_read_001yes__i_can_rea          0.407
## E_Can_you_read_001yes__i_can_som          0.659
## subjects.per.village                      0.575
## subcounty.blockingKanungu Town Council    0.444
## subcounty.blockingKayonza                 0.034
## subcounty.blockingKinaaba                 0.392
## subcounty.blockingKirima                  0.224
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.174
## subcounty.blockingRuhija                  0.003
## subcounty.blockingSingles                 0.348</code></pre>
<pre class="r"><code>mod.h8.1.spill2 &lt;- ols(e.participate.b10 ~ treat + S2 + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.1.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.075      0.449   6.851
## treat                                    -0.148      0.064  -2.329
## S21                                       0.168      0.267   0.630
## S22                                       0.127      0.304   0.419
## S23                                       0.000      0.314  -0.001
## S24                                      -0.061      0.343  -0.179
## b.participate.b10                         0.107      0.040   2.709
## E_subject_s_gendermale                    0.169      0.096   1.764
## E_Subject_s_age                           0.006      0.003   1.720
## E_Subject_s_income100_000___200_         -0.159      0.320  -0.497
## E_Subject_s_income20_000___100_0         -0.134      0.337  -0.399
## E_Subject_s_income20_000_shillin         -0.226      0.353  -0.642
## E_Subject_s_income200_000___500_         -0.170      0.335  -0.507
## E_Subject_s_income500_000___1_mi          0.229      0.331   0.692
## E_Subject_s_incomerefused_to_ans         -0.750      0.502  -1.494
## E_Can_you_read_001no__i_cannot_r         -0.245      0.204  -1.202
## E_Can_you_read_001yes__i_can_rea         -0.106      0.096  -1.103
## E_Can_you_read_001yes__i_can_som         -0.072      0.113  -0.638
## subjects.per.village                     -0.002      0.005  -0.456
## subcounty.blockingKanungu Town Council    0.199      0.086   2.311
## subcounty.blockingKayonza                -0.126      0.055  -2.285
## subcounty.blockingKinaaba                 0.023      0.110   0.212
## subcounty.blockingKirima                 -0.007      0.031  -0.218
## subcounty.blockingKirundo                -0.248      0.066  -3.759
## subcounty.blockingMpungu                  0.038      0.038   0.994
## subcounty.blockingNyabwishenya           -0.095      0.059  -1.612
## subcounty.blockingRuhija                  0.020      0.024   0.816
## subcounty.blockingSingles                -0.098      0.074  -1.329
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.020
## S21                                       0.529
## S22                                       0.676
## S23                                       0.999
## S24                                       0.858
## b.participate.b10                         0.007
## E_subject_s_gendermale                    0.078
## E_Subject_s_age                           0.085
## E_Subject_s_income100_000___200_          0.619
## E_Subject_s_income20_000___100_0          0.690
## E_Subject_s_income20_000_shillin          0.521
## E_Subject_s_income200_000___500_          0.612
## E_Subject_s_income500_000___1_mi          0.489
## E_Subject_s_incomerefused_to_ans          0.135
## E_Can_you_read_001no__i_cannot_r          0.229
## E_Can_you_read_001yes__i_can_rea          0.270
## E_Can_you_read_001yes__i_can_som          0.523
## subjects.per.village                      0.648
## subcounty.blockingKanungu Town Council    0.021
## subcounty.blockingKayonza                 0.022
## subcounty.blockingKinaaba                 0.832
## subcounty.blockingKirima                  0.827
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.320
## subcounty.blockingNyabwishenya            0.107
## subcounty.blockingRuhija                  0.414
## subcounty.blockingSingles                 0.184</code></pre>
<pre class="r"><code>mod.h8.1.spill3 &lt;- ols(e.participate.b10 ~ treat + S3 + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.1.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.265      0.502   6.508
## treat                                    -0.123      0.073  -1.678
## S32                                      -0.132      0.144  -0.920
## S33                                      -0.038      0.212  -0.179
## S34                                      -0.080      0.224  -0.355
## S35                                       0.009      0.231   0.037
## S36                                      -0.316      0.266  -1.186
## b.participate.b10                         0.100      0.037   2.702
## E_subject_s_gendermale                    0.163      0.098   1.652
## E_Subject_s_age                           0.006      0.003   1.805
## E_Subject_s_income100_000___200_         -0.193      0.313  -0.618
## E_Subject_s_income20_000___100_0         -0.174      0.332  -0.523
## E_Subject_s_income20_000_shillin         -0.269      0.348  -0.774
## E_Subject_s_income200_000___500_         -0.214      0.329  -0.650
## E_Subject_s_income500_000___1_mi          0.183      0.339   0.540
## E_Subject_s_incomerefused_to_ans         -0.765      0.492  -1.553
## E_Can_you_read_001no__i_cannot_r         -0.228      0.195  -1.169
## E_Can_you_read_001yes__i_can_rea         -0.098      0.102  -0.961
## E_Can_you_read_001yes__i_can_som         -0.063      0.116  -0.549
## subjects.per.village                     -0.002      0.005  -0.492
## subcounty.blockingKanungu Town Council    0.096      0.073   1.317
## subcounty.blockingKayonza                -0.091      0.051  -1.808
## subcounty.blockingKinaaba                 0.057      0.116   0.489
## subcounty.blockingKirima                 -0.034      0.037  -0.911
## subcounty.blockingKirundo                -0.194      0.074  -2.616
## subcounty.blockingMpungu                  0.065      0.036   1.830
## subcounty.blockingNyabwishenya           -0.078      0.048  -1.641
## subcounty.blockingRuhija                  0.064      0.031   2.042
## subcounty.blockingSingles                -0.054      0.134  -0.402
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.093
## S32                                       0.358
## S33                                       0.858
## S34                                       0.722
## S35                                       0.971
## S36                                       0.236
## b.participate.b10                         0.007
## E_subject_s_gendermale                    0.099
## E_Subject_s_age                           0.071
## E_Subject_s_income100_000___200_          0.537
## E_Subject_s_income20_000___100_0          0.601
## E_Subject_s_income20_000_shillin          0.439
## E_Subject_s_income200_000___500_          0.516
## E_Subject_s_income500_000___1_mi          0.590
## E_Subject_s_incomerefused_to_ans          0.120
## E_Can_you_read_001no__i_cannot_r          0.242
## E_Can_you_read_001yes__i_can_rea          0.336
## E_Can_you_read_001yes__i_can_som          0.583
## subjects.per.village                      0.623
## subcounty.blockingKanungu Town Council    0.188
## subcounty.blockingKayonza                 0.071
## subcounty.blockingKinaaba                 0.625
## subcounty.blockingKirima                  0.362
## subcounty.blockingKirundo                 0.009
## subcounty.blockingMpungu                  0.067
## subcounty.blockingNyabwishenya            0.101
## subcounty.blockingRuhija                  0.041
## subcounty.blockingSingles                 0.688</code></pre>
<pre class="r"><code>###H8.3: Participate in RS meetings in past months
mod.h8.3 &lt;- ols(e.participate.e4 ~ treat + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               0.868      0.208   4.165
## treat                                    -0.047      0.025  -1.878
## E_subject_s_gendermale                    0.060      0.045   1.327
## E_Subject_s_age                           0.001      0.001   1.181
## E_Subject_s_income100_000___200_         -0.117      0.142  -0.824
## E_Subject_s_income20_000___100_0         -0.089      0.143  -0.617
## E_Subject_s_income20_000_shillin         -0.164      0.159  -1.032
## E_Subject_s_income200_000___500_         -0.196      0.156  -1.258
## E_Subject_s_income500_000___1_mi         -0.162      0.172  -0.943
## E_Subject_s_incomerefused_to_ans         -0.043      0.189  -0.228
## E_Can_you_read_001no__i_cannot_r         -0.264      0.077  -3.423
## E_Can_you_read_001yes__i_can_rea         -0.094      0.064  -1.471
## E_Can_you_read_001yes__i_can_som         -0.006      0.039  -0.154
## subjects.per.village                     -0.003      0.002  -1.677
## subcounty.blockingKanungu Town Council    0.094      0.018   5.369
## subcounty.blockingKayonza                 0.148      0.016   8.985
## subcounty.blockingKinaaba                 0.114      0.037   3.101
## subcounty.blockingKirima                  0.046      0.008   5.805
## subcounty.blockingKirundo                -0.019      0.021  -0.912
## subcounty.blockingMpungu                  0.037      0.009   4.370
## subcounty.blockingNyabwishenya            0.009      0.013   0.684
## subcounty.blockingRuhija                  0.057      0.008   7.574
## subcounty.blockingSingles                 0.146      0.022   6.550
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.060
## E_subject_s_gendermale                    0.184
## E_Subject_s_age                           0.238
## E_Subject_s_income100_000___200_          0.410
## E_Subject_s_income20_000___100_0          0.537
## E_Subject_s_income20_000_shillin          0.302
## E_Subject_s_income200_000___500_          0.209
## E_Subject_s_income500_000___1_mi          0.345
## E_Subject_s_incomerefused_to_ans          0.820
## E_Can_you_read_001no__i_cannot_r          0.001
## E_Can_you_read_001yes__i_can_rea          0.141
## E_Can_you_read_001yes__i_can_som          0.877
## subjects.per.village                      0.093
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.002
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.362
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.494
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h8.3.spill &lt;- ols(e.participate.e4 ~ treat + S1 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.3.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               0.790      0.199   3.962
## treat                                    -0.033      0.021  -1.525
## S11                                       0.072      0.050   1.431
## S12                                       0.103      0.031   3.290
## E_subject_s_gendermale                    0.061      0.042   1.460
## E_Subject_s_age                           0.001      0.001   1.364
## E_Subject_s_income100_000___200_         -0.123      0.131  -0.941
## E_Subject_s_income20_000___100_0         -0.101      0.136  -0.741
## E_Subject_s_income20_000_shillin         -0.175      0.155  -1.129
## E_Subject_s_income200_000___500_         -0.208      0.147  -1.420
## E_Subject_s_income500_000___1_mi         -0.164      0.168  -0.978
## E_Subject_s_incomerefused_to_ans         -0.035      0.170  -0.209
## E_Can_you_read_001no__i_cannot_r         -0.255      0.078  -3.257
## E_Can_you_read_001yes__i_can_rea         -0.087      0.060  -1.462
## E_Can_you_read_001yes__i_can_som          0.002      0.040   0.049
## subjects.per.village                     -0.003      0.001  -2.345
## subcounty.blockingKanungu Town Council    0.071      0.031   2.267
## subcounty.blockingKayonza                 0.152      0.015  10.413
## subcounty.blockingKinaaba                 0.162      0.035   4.696
## subcounty.blockingKirima                  0.044      0.019   2.264
## subcounty.blockingKirundo                -0.005      0.020  -0.234
## subcounty.blockingMpungu                  0.050      0.006   8.110
## subcounty.blockingNyabwishenya            0.006      0.019   0.323
## subcounty.blockingRuhija                  0.068      0.013   5.304
## subcounty.blockingSingles                 0.190      0.026   7.355
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.127
## S11                                       0.153
## S12                                       0.001
## E_subject_s_gendermale                    0.144
## E_Subject_s_age                           0.173
## E_Subject_s_income100_000___200_          0.347
## E_Subject_s_income20_000___100_0          0.459
## E_Subject_s_income20_000_shillin          0.259
## E_Subject_s_income200_000___500_          0.156
## E_Subject_s_income500_000___1_mi          0.328
## E_Subject_s_incomerefused_to_ans          0.835
## E_Can_you_read_001no__i_cannot_r          0.001
## E_Can_you_read_001yes__i_can_rea          0.144
## E_Can_you_read_001yes__i_can_som          0.961
## subjects.per.village                      0.019
## subcounty.blockingKanungu Town Council    0.023
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.024
## subcounty.blockingKirundo                 0.815
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.747
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h8.3.spill2 &lt;- ols(e.participate.e4 ~ treat + S2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.3.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               0.738      0.201   3.668
## treat                                    -0.039      0.023  -1.648
## S21                                       0.115      0.138   0.832
## S22                                       0.154      0.154   0.997
## S23                                       0.154      0.132   1.166
## S24                                       0.136      0.091   1.499
## E_subject_s_gendermale                    0.062      0.043   1.448
## E_Subject_s_age                           0.001      0.001   1.170
## E_Subject_s_income100_000___200_         -0.120      0.140  -0.861
## E_Subject_s_income20_000___100_0         -0.092      0.143  -0.645
## E_Subject_s_income20_000_shillin         -0.170      0.159  -1.068
## E_Subject_s_income200_000___500_         -0.200      0.154  -1.299
## E_Subject_s_income500_000___1_mi         -0.155      0.163  -0.954
## E_Subject_s_incomerefused_to_ans         -0.047      0.183  -0.259
## E_Can_you_read_001no__i_cannot_r         -0.265      0.077  -3.449
## E_Can_you_read_001yes__i_can_rea         -0.094      0.062  -1.525
## E_Can_you_read_001yes__i_can_som         -0.005      0.040  -0.122
## subjects.per.village                     -0.004      0.002  -2.065
## subcounty.blockingKanungu Town Council    0.085      0.031   2.746
## subcounty.blockingKayonza                 0.153      0.023   6.756
## subcounty.blockingKinaaba                 0.124      0.036   3.435
## subcounty.blockingKirima                  0.041      0.016   2.520
## subcounty.blockingKirundo                -0.027      0.032  -0.855
## subcounty.blockingMpungu                  0.042      0.017   2.423
## subcounty.blockingNyabwishenya           -0.001      0.023  -0.027
## subcounty.blockingRuhija                  0.053      0.020   2.641
## subcounty.blockingSingles                 0.179      0.037   4.822
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.099
## S21                                       0.405
## S22                                       0.319
## S23                                       0.244
## S24                                       0.134
## E_subject_s_gendermale                    0.148
## E_Subject_s_age                           0.242
## E_Subject_s_income100_000___200_          0.389
## E_Subject_s_income20_000___100_0          0.519
## E_Subject_s_income20_000_shillin          0.285
## E_Subject_s_income200_000___500_          0.194
## E_Subject_s_income500_000___1_mi          0.340
## E_Subject_s_incomerefused_to_ans          0.795
## E_Can_you_read_001no__i_cannot_r          0.001
## E_Can_you_read_001yes__i_can_rea          0.127
## E_Can_you_read_001yes__i_can_som          0.903
## subjects.per.village                      0.039
## subcounty.blockingKanungu Town Council    0.006
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.001
## subcounty.blockingKirima                  0.012
## subcounty.blockingKirundo                 0.393
## subcounty.blockingMpungu                  0.015
## subcounty.blockingNyabwishenya            0.978
## subcounty.blockingRuhija                  0.008
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>mod.h8.3.spill3 &lt;- ols(e.participate.e4 ~ treat + S3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h8.3.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               0.763      0.234   3.267
## treat                                    -0.029      0.032  -0.906
## S32                                       0.097      0.054   1.811
## S33                                       0.109      0.075   1.454
## S34                                       0.186      0.099   1.872
## S35                                       0.144      0.082   1.751
## S36                                      -0.032      0.098  -0.330
## E_subject_s_gendermale                    0.053      0.044   1.188
## E_Subject_s_age                           0.001      0.001   1.389
## E_Subject_s_income100_000___200_         -0.131      0.144  -0.909
## E_Subject_s_income20_000___100_0         -0.101      0.148  -0.683
## E_Subject_s_income20_000_shillin         -0.181      0.165  -1.101
## E_Subject_s_income200_000___500_         -0.209      0.158  -1.317
## E_Subject_s_income500_000___1_mi         -0.153      0.176  -0.869
## E_Subject_s_incomerefused_to_ans         -0.054      0.186  -0.288
## E_Can_you_read_001no__i_cannot_r         -0.264      0.073  -3.626
## E_Can_you_read_001yes__i_can_rea         -0.089      0.061  -1.449
## E_Can_you_read_001yes__i_can_som         -0.004      0.041  -0.103
## subjects.per.village                     -0.004      0.001  -2.594
## subcounty.blockingKanungu Town Council    0.032      0.039   0.826
## subcounty.blockingKayonza                 0.147      0.016   9.361
## subcounty.blockingKinaaba                 0.144      0.054   2.638
## subcounty.blockingKirima                  0.037      0.013   2.730
## subcounty.blockingKirundo                -0.019      0.025  -0.763
## subcounty.blockingMpungu                  0.041      0.011   3.663
## subcounty.blockingNyabwishenya           -0.012      0.029  -0.424
## subcounty.blockingRuhija                  0.060      0.009   6.520
## subcounty.blockingSingles                 0.208      0.047   4.409
##                                        Pr(&gt;|t|)
## (Intercept)                               0.001
## treat                                     0.365
## S32                                       0.070
## S33                                       0.146
## S34                                       0.061
## S35                                       0.080
## S36                                       0.741
## E_subject_s_gendermale                    0.235
## E_Subject_s_age                           0.165
## E_Subject_s_income100_000___200_          0.364
## E_Subject_s_income20_000___100_0          0.494
## E_Subject_s_income20_000_shillin          0.271
## E_Subject_s_income200_000___500_          0.188
## E_Subject_s_income500_000___1_mi          0.385
## E_Subject_s_incomerefused_to_ans          0.773
## E_Can_you_read_001no__i_cannot_r          0.000
## E_Can_you_read_001yes__i_can_rea          0.147
## E_Can_you_read_001yes__i_can_som          0.918
## subjects.per.village                      0.009
## subcounty.blockingKanungu Town Council    0.409
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.008
## subcounty.blockingKirima                  0.006
## subcounty.blockingKirundo                 0.446
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.672
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>###H7.1: Satisfaction with RS Info from UWA
#Note: this row is labeled incorrectly in the online SI
mod.h7 &lt;- ols(e.knowledge.b7 ~ treat + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.605      0.253  14.275
## treat                                     0.014      0.055   0.254
## b.knowledge.b7                            0.100      0.021   4.688
## E_subject_s_gendermale                    0.178      0.058   3.081
## E_Subject_s_age                           0.000      0.002   0.048
## E_Subject_s_income100_000___200_         -0.164      0.149  -1.102
## E_Subject_s_income20_000___100_0         -0.210      0.167  -1.253
## E_Subject_s_income20_000_shillin         -0.323      0.157  -2.061
## E_Subject_s_income200_000___500_         -0.164      0.140  -1.174
## E_Subject_s_income500_000___1_mi         -0.222      0.198  -1.121
## E_Subject_s_incomerefused_to_ans         -1.129      0.582  -1.938
## E_Can_you_read_001no__i_cannot_r          0.080      0.216   0.368
## E_Can_you_read_001yes__i_can_rea          0.333      0.146   2.280
## E_Can_you_read_001yes__i_can_som          0.205      0.110   1.854
## subjects.per.village                     -0.002      0.002  -1.327
## subcounty.blockingKanungu Town Council    0.178      0.021   8.351
## subcounty.blockingKayonza                -0.143      0.016  -9.192
## subcounty.blockingKinaaba                 0.010      0.059   0.167
## subcounty.blockingKirima                  0.085      0.019   4.403
## subcounty.blockingKirundo                 0.105      0.036   2.919
## subcounty.blockingMpungu                 -0.064      0.027  -2.369
## subcounty.blockingNyabwishenya           -0.080      0.029  -2.746
## subcounty.blockingRuhija                 -0.122      0.018  -6.741
## subcounty.blockingSingles                 0.002      0.029   0.076
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.799
## b.knowledge.b7                            0.000
## E_subject_s_gendermale                    0.002
## E_Subject_s_age                           0.962
## E_Subject_s_income100_000___200_          0.271
## E_Subject_s_income20_000___100_0          0.210
## E_Subject_s_income20_000_shillin          0.039
## E_Subject_s_income200_000___500_          0.240
## E_Subject_s_income500_000___1_mi          0.262
## E_Subject_s_incomerefused_to_ans          0.053
## E_Can_you_read_001no__i_cannot_r          0.713
## E_Can_you_read_001yes__i_can_rea          0.023
## E_Can_you_read_001yes__i_can_som          0.064
## subjects.per.village                      0.184
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.868
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.004
## subcounty.blockingMpungu                  0.018
## subcounty.blockingNyabwishenya            0.006
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.939</code></pre>
<pre class="r"><code>mod.h7.spill &lt;- ols(e.knowledge.b7 ~ treat + S1 + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.595      0.257  13.976
## treat                                     0.000      0.043   0.002
## S11                                      -0.089      0.072  -1.225
## S12                                      -0.030      0.107  -0.280
## b.knowledge.b7                            0.101      0.020   5.100
## E_subject_s_gendermale                    0.175      0.056   3.099
## E_Subject_s_age                           0.000      0.003   0.023
## E_Subject_s_income100_000___200_         -0.130      0.138  -0.943
## E_Subject_s_income20_000___100_0         -0.177      0.161  -1.097
## E_Subject_s_income20_000_shillin         -0.296      0.164  -1.807
## E_Subject_s_income200_000___500_         -0.129      0.133  -0.966
## E_Subject_s_income500_000___1_mi         -0.208      0.194  -1.075
## E_Subject_s_incomerefused_to_ans         -1.117      0.577  -1.937
## E_Can_you_read_001no__i_cannot_r          0.072      0.221   0.326
## E_Can_you_read_001yes__i_can_rea          0.332      0.147   2.261
## E_Can_you_read_001yes__i_can_som          0.203      0.112   1.816
## subcounty.blockingKanungu Town Council    0.175      0.050   3.524
## subcounty.blockingKayonza                -0.131      0.008 -15.815
## subcounty.blockingKinaaba                -0.076      0.035  -2.149
## subcounty.blockingKirima                  0.064      0.035   1.816
## subcounty.blockingKirundo                 0.106      0.027   3.933
## subcounty.blockingMpungu                 -0.078      0.024  -3.265
## subcounty.blockingNyabwishenya           -0.053      0.033  -1.605
## subcounty.blockingRuhija                 -0.143      0.018  -7.751
## subcounty.blockingSingles                -0.008      0.039  -0.203
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.998
## S11                                       0.221
## S12                                       0.779
## b.knowledge.b7                            0.000
## E_subject_s_gendermale                    0.002
## E_Subject_s_age                           0.982
## E_Subject_s_income100_000___200_          0.346
## E_Subject_s_income20_000___100_0          0.273
## E_Subject_s_income20_000_shillin          0.071
## E_Subject_s_income200_000___500_          0.334
## E_Subject_s_income500_000___1_mi          0.282
## E_Subject_s_incomerefused_to_ans          0.053
## E_Can_you_read_001no__i_cannot_r          0.745
## E_Can_you_read_001yes__i_can_rea          0.024
## E_Can_you_read_001yes__i_can_som          0.069
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.032
## subcounty.blockingKirima                  0.069
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.001
## subcounty.blockingNyabwishenya            0.108
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.839</code></pre>
<pre class="r"><code>mod.h7.spill2 &lt;- ols(e.knowledge.b7 ~ treat + S2 + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.721      0.329  11.308
## treat                                    -0.013      0.045  -0.296
## S21                                      -0.092      0.191  -0.482
## S22                                      -0.131      0.176  -0.744
## S23                                      -0.209      0.161  -1.302
## S24                                      -0.310      0.131  -2.373
## b.knowledge.b7                            0.100      0.021   4.681
## E_subject_s_gendermale                    0.182      0.058   3.162
## E_Subject_s_age                           0.000      0.002   0.050
## E_Subject_s_income100_000___200_         -0.155      0.146  -1.063
## E_Subject_s_income20_000___100_0         -0.200      0.164  -1.215
## E_Subject_s_income20_000_shillin         -0.309      0.160  -1.933
## E_Subject_s_income200_000___500_         -0.152      0.140  -1.088
## E_Subject_s_income500_000___1_mi         -0.210      0.216  -0.973
## E_Subject_s_incomerefused_to_ans         -1.120      0.584  -1.916
## E_Can_you_read_001no__i_cannot_r          0.073      0.218   0.334
## E_Can_you_read_001yes__i_can_rea          0.323      0.149   2.168
## E_Can_you_read_001yes__i_can_som          0.193      0.112   1.725
## subcounty.blockingKanungu Town Council    0.275      0.049   5.632
## subcounty.blockingKayonza                -0.155      0.019  -8.004
## subcounty.blockingKinaaba                -0.079      0.024  -3.240
## subcounty.blockingKirima                  0.091      0.020   4.670
## subcounty.blockingKirundo                 0.099      0.029   3.374
## subcounty.blockingMpungu                 -0.094      0.017  -5.573
## subcounty.blockingNyabwishenya           -0.068      0.027  -2.506
## subcounty.blockingRuhija                 -0.148      0.013 -11.000
## subcounty.blockingSingles                -0.045      0.036  -1.230
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.767
## S21                                       0.630
## S22                                       0.457
## S23                                       0.193
## S24                                       0.018
## b.knowledge.b7                            0.000
## E_subject_s_gendermale                    0.002
## E_Subject_s_age                           0.960
## E_Subject_s_income100_000___200_          0.288
## E_Subject_s_income20_000___100_0          0.224
## E_Subject_s_income20_000_shillin          0.053
## E_Subject_s_income200_000___500_          0.276
## E_Subject_s_income500_000___1_mi          0.331
## E_Subject_s_incomerefused_to_ans          0.055
## E_Can_you_read_001no__i_cannot_r          0.738
## E_Can_you_read_001yes__i_can_rea          0.030
## E_Can_you_read_001yes__i_can_som          0.084
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.001
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.001
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.012
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.219</code></pre>
<pre class="r"><code>mod.h7.spill3 &lt;- ols(e.knowledge.b7 ~ treat + S3 + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
mod.h7.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.714      0.453   8.206
## treat                                    -0.039      0.030  -1.287
## S32                                       0.006      0.249   0.024
## S33                                      -0.087      0.269  -0.323
## S34                                      -0.169      0.277  -0.611
## S35                                      -0.115      0.278  -0.413
## S36                                      -0.359      0.256  -1.400
## b.knowledge.b7                            0.104      0.020   5.137
## E_subject_s_gendermale                    0.183      0.058   3.171
## E_Subject_s_age                           0.000      0.003  -0.079
## E_Subject_s_income100_000___200_         -0.166      0.135  -1.235
## E_Subject_s_income20_000___100_0         -0.213      0.154  -1.386
## E_Subject_s_income20_000_shillin         -0.324      0.160  -2.031
## E_Subject_s_income200_000___500_         -0.163      0.130  -1.255
## E_Subject_s_income500_000___1_mi         -0.213      0.206  -1.033
## E_Subject_s_incomerefused_to_ans         -1.149      0.590  -1.946
## E_Can_you_read_001no__i_cannot_r          0.069      0.213   0.324
## E_Can_you_read_001yes__i_can_rea          0.315      0.146   2.150
## E_Can_you_read_001yes__i_can_som          0.183      0.109   1.683
## subcounty.blockingKanungu Town Council    0.231      0.060   3.864
## subcounty.blockingKayonza                -0.172      0.011 -16.133
## subcounty.blockingKinaaba                -0.085      0.023  -3.767
## subcounty.blockingKirima                  0.064      0.028   2.307
## subcounty.blockingKirundo                 0.051      0.026   1.974
## subcounty.blockingMpungu                 -0.110      0.026  -4.235
## subcounty.blockingNyabwishenya           -0.110      0.030  -3.675
## subcounty.blockingRuhija                 -0.173      0.015 -11.842
## subcounty.blockingSingles                -0.089      0.130  -0.681
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.198
## S32                                       0.981
## S33                                       0.746
## S34                                       0.541
## S35                                       0.680
## S36                                       0.162
## b.knowledge.b7                            0.000
## E_subject_s_gendermale                    0.002
## E_Subject_s_age                           0.937
## E_Subject_s_income100_000___200_          0.217
## E_Subject_s_income20_000___100_0          0.166
## E_Subject_s_income20_000_shillin          0.042
## E_Subject_s_income200_000___500_          0.209
## E_Subject_s_income500_000___1_mi          0.301
## E_Subject_s_incomerefused_to_ans          0.052
## E_Can_you_read_001no__i_cannot_r          0.746
## E_Can_you_read_001yes__i_can_rea          0.032
## E_Can_you_read_001yes__i_can_som          0.092
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.021
## subcounty.blockingKirundo                 0.048
## subcounty.blockingMpungu                  0.000
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.496</code></pre>
<pre class="r"><code>###H11: Satisfaction with RS
nod.h11 &lt;- ols(e.satisfaction.b3 ~ treat + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h11</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.770      0.677   4.094
## treat                                    -0.030      0.082  -0.362
## b.satisfaction.b3                         0.206      0.040   5.121
## E_subject_s_gendermale                   -0.033      0.065  -0.519
## E_Subject_s_age                           0.004      0.002   1.730
## E_Subject_s_income100_000___200_          0.349      0.560   0.624
## E_Subject_s_income20_000___100_0          0.227      0.475   0.478
## E_Subject_s_income20_000_shillin          0.318      0.511   0.622
## E_Subject_s_income200_000___500_          0.215      0.505   0.426
## E_Subject_s_income500_000___1_mi          0.093      0.634   0.146
## E_Subject_s_incomerefused_to_ans         -0.947      0.803  -1.179
## E_Can_you_read_001no__i_cannot_r         -0.245      0.261  -0.938
## E_Can_you_read_001yes__i_can_rea         -0.194      0.137  -1.419
## E_Can_you_read_001yes__i_can_som         -0.251      0.158  -1.584
## subjects.per.village                     -0.007      0.006  -1.227
## subcounty.blockingKanungu Town Council   -0.036      0.044  -0.813
## subcounty.blockingKayonza                 0.139      0.051   2.699
## subcounty.blockingKinaaba                 0.475      0.129   3.671
## subcounty.blockingKirima                  0.283      0.023  12.204
## subcounty.blockingKirundo                 0.412      0.049   8.428
## subcounty.blockingMpungu                 -0.015      0.019  -0.827
## subcounty.blockingNyabwishenya            0.346      0.029  12.089
## subcounty.blockingRuhija                  0.158      0.006  27.598
## subcounty.blockingSingles                 0.410      0.072   5.654
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.717
## b.satisfaction.b3                         0.000
## E_subject_s_gendermale                    0.604
## E_Subject_s_age                           0.084
## E_Subject_s_income100_000___200_          0.533
## E_Subject_s_income20_000___100_0          0.633
## E_Subject_s_income20_000_shillin          0.534
## E_Subject_s_income200_000___500_          0.670
## E_Subject_s_income500_000___1_mi          0.884
## E_Subject_s_incomerefused_to_ans          0.238
## E_Can_you_read_001no__i_cannot_r          0.348
## E_Can_you_read_001yes__i_can_rea          0.156
## E_Can_you_read_001yes__i_can_som          0.113
## subjects.per.village                      0.220
## subcounty.blockingKanungu Town Council    0.416
## subcounty.blockingKayonza                 0.007
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.408
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>nod.h11.spill &lt;- ols(e.satisfaction.b3 ~ treat + S1 + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h11.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.978      0.595   5.010
## treat                                    -0.076      0.068  -1.103
## S11                                      -0.284      0.086  -3.310
## S12                                      -0.283      0.225  -1.257
## b.satisfaction.b3                         0.215      0.039   5.571
## E_subject_s_gendermale                   -0.044      0.065  -0.684
## E_Subject_s_age                           0.004      0.002   1.502
## E_Subject_s_income100_000___200_          0.404      0.520   0.776
## E_Subject_s_income20_000___100_0          0.297      0.439   0.677
## E_Subject_s_income20_000_shillin          0.375      0.483   0.777
## E_Subject_s_income200_000___500_          0.288      0.467   0.617
## E_Subject_s_income500_000___1_mi          0.121      0.614   0.197
## E_Subject_s_incomerefused_to_ans         -0.916      0.755  -1.213
## E_Can_you_read_001no__i_cannot_r         -0.279      0.261  -1.067
## E_Can_you_read_001yes__i_can_rea         -0.208      0.143  -1.453
## E_Can_you_read_001yes__i_can_som         -0.270      0.165  -1.640
## subjects.per.village                     -0.007      0.006  -1.185
## subcounty.blockingKanungu Town Council   -0.007      0.127  -0.052
## subcounty.blockingKayonza                 0.117      0.050   2.339
## subcounty.blockingKinaaba                 0.300      0.158   1.896
## subcounty.blockingKirima                  0.253      0.055   4.607
## subcounty.blockingKirundo                 0.353      0.042   8.345
## subcounty.blockingMpungu                 -0.060      0.024  -2.472
## subcounty.blockingNyabwishenya            0.377      0.026  14.527
## subcounty.blockingRuhija                  0.105      0.024   4.417
## subcounty.blockingSingles                 0.258      0.051   5.098
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.270
## S11                                       0.001
## S12                                       0.209
## b.satisfaction.b3                         0.000
## E_subject_s_gendermale                    0.494
## E_Subject_s_age                           0.133
## E_Subject_s_income100_000___200_          0.438
## E_Subject_s_income20_000___100_0          0.498
## E_Subject_s_income20_000_shillin          0.437
## E_Subject_s_income200_000___500_          0.537
## E_Subject_s_income500_000___1_mi          0.844
## E_Subject_s_incomerefused_to_ans          0.225
## E_Can_you_read_001no__i_cannot_r          0.286
## E_Can_you_read_001yes__i_can_rea          0.146
## E_Can_you_read_001yes__i_can_som          0.101
## subjects.per.village                      0.236
## subcounty.blockingKanungu Town Council    0.959
## subcounty.blockingKayonza                 0.019
## subcounty.blockingKinaaba                 0.058
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.013
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>nod.h11.spill2 &lt;- ols(e.satisfaction.b3 ~ treat + S2 + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h11.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.952      0.726   4.066
## treat                                    -0.088      0.084  -1.039
## S21                                      -0.058      0.272  -0.214
## S22                                      -0.051      0.220  -0.230
## S23                                      -0.316      0.219  -1.446
## S24                                      -0.464      0.222  -2.086
## b.satisfaction.b3                         0.204      0.038   5.337
## E_subject_s_gendermale                   -0.020      0.065  -0.304
## E_Subject_s_age                           0.004      0.002   1.600
## E_Subject_s_income100_000___200_          0.355      0.538   0.660
## E_Subject_s_income20_000___100_0          0.243      0.462   0.526
## E_Subject_s_income20_000_shillin          0.336      0.497   0.676
## E_Subject_s_income200_000___500_          0.234      0.497   0.471
## E_Subject_s_income500_000___1_mi          0.130      0.658   0.197
## E_Subject_s_incomerefused_to_ans         -0.937      0.797  -1.176
## E_Can_you_read_001no__i_cannot_r         -0.254      0.263  -0.963
## E_Can_you_read_001yes__i_can_rea         -0.215      0.138  -1.554
## E_Can_you_read_001yes__i_can_som         -0.272      0.162  -1.681
## subjects.per.village                     -0.006      0.006  -0.946
## subcounty.blockingKanungu Town Council    0.165      0.087   1.882
## subcounty.blockingKayonza                 0.078      0.070   1.112
## subcounty.blockingKinaaba                 0.328      0.152   2.162
## subcounty.blockingKirima                  0.297      0.039   7.619
## subcounty.blockingKirundo                 0.342      0.067   5.115
## subcounty.blockingMpungu                 -0.067      0.038  -1.769
## subcounty.blockingNyabwishenya            0.363      0.043   8.527
## subcounty.blockingRuhija                  0.082      0.034   2.413
## subcounty.blockingSingles                 0.305      0.095   3.219
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.299
## S21                                       0.830
## S22                                       0.818
## S23                                       0.148
## S24                                       0.037
## b.satisfaction.b3                         0.000
## E_subject_s_gendermale                    0.761
## E_Subject_s_age                           0.110
## E_Subject_s_income100_000___200_          0.509
## E_Subject_s_income20_000___100_0          0.599
## E_Subject_s_income20_000_shillin          0.499
## E_Subject_s_income200_000___500_          0.637
## E_Subject_s_income500_000___1_mi          0.843
## E_Subject_s_incomerefused_to_ans          0.240
## E_Can_you_read_001no__i_cannot_r          0.335
## E_Can_you_read_001yes__i_can_rea          0.120
## E_Can_you_read_001yes__i_can_som          0.093
## subjects.per.village                      0.344
## subcounty.blockingKanungu Town Council    0.060
## subcounty.blockingKayonza                 0.266
## subcounty.blockingKinaaba                 0.031
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.077
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.016
## subcounty.blockingSingles                 0.001</code></pre>
<pre class="r"><code>nod.h11.spill3 &lt;- ols(e.satisfaction.b3 ~ treat + S3 + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h11.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               2.407      0.562   4.284
## treat                                    -0.073      0.098  -0.747
## S32                                       0.524      0.182   2.886
## S33                                       0.469      0.233   2.011
## S34                                       0.353      0.278   1.270
## S35                                       0.432      0.466   0.928
## S36                                       0.041      0.275   0.150
## b.satisfaction.b3                         0.214      0.040   5.410
## E_subject_s_gendermale                   -0.031      0.056  -0.567
## E_Subject_s_age                           0.004      0.003   1.357
## E_Subject_s_income100_000___200_          0.341      0.531   0.642
## E_Subject_s_income20_000___100_0          0.223      0.454   0.492
## E_Subject_s_income20_000_shillin          0.296      0.495   0.598
## E_Subject_s_income200_000___500_          0.221      0.483   0.458
## E_Subject_s_income500_000___1_mi          0.138      0.639   0.216
## E_Subject_s_incomerefused_to_ans         -0.955      0.788  -1.212
## E_Can_you_read_001no__i_cannot_r         -0.276      0.259  -1.063
## E_Can_you_read_001yes__i_can_rea         -0.214      0.131  -1.630
## E_Can_you_read_001yes__i_can_som         -0.277      0.157  -1.763
## subjects.per.village                     -0.007      0.006  -1.159
## subcounty.blockingKanungu Town Council   -0.006      0.125  -0.045
## subcounty.blockingKayonza                 0.089      0.062   1.421
## subcounty.blockingKinaaba                 0.411      0.159   2.589
## subcounty.blockingKirima                  0.239      0.100   2.384
## subcounty.blockingKirundo                 0.325      0.084   3.872
## subcounty.blockingMpungu                 -0.025      0.033  -0.743
## subcounty.blockingNyabwishenya            0.310      0.041   7.617
## subcounty.blockingRuhija                  0.097      0.037   2.618
## subcounty.blockingSingles                 0.555      0.169   3.294
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.455
## S32                                       0.004
## S33                                       0.044
## S34                                       0.204
## S35                                       0.354
## S36                                       0.881
## b.satisfaction.b3                         0.000
## E_subject_s_gendermale                    0.571
## E_Subject_s_age                           0.175
## E_Subject_s_income100_000___200_          0.521
## E_Subject_s_income20_000___100_0          0.623
## E_Subject_s_income20_000_shillin          0.550
## E_Subject_s_income200_000___500_          0.647
## E_Subject_s_income500_000___1_mi          0.829
## E_Subject_s_incomerefused_to_ans          0.226
## E_Can_you_read_001no__i_cannot_r          0.288
## E_Can_you_read_001yes__i_can_rea          0.103
## E_Can_you_read_001yes__i_can_som          0.078
## subjects.per.village                      0.247
## subcounty.blockingKanungu Town Council    0.964
## subcounty.blockingKayonza                 0.155
## subcounty.blockingKinaaba                 0.010
## subcounty.blockingKirima                  0.017
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.457
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.009
## subcounty.blockingSingles                 0.001</code></pre>
<pre class="r"><code>###H10: Satisfaction with Park Management
nod.h10mgmt &lt;- ols(e.satisfaction.b2 ~ treat + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h10mgmt</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.263      0.310  10.524
## treat                                     0.013      0.079   0.168
## b.satisfaction.b2                         0.171      0.060   2.837
## E_subject_s_gendermale                   -0.031      0.084  -0.375
## E_Subject_s_age                           0.004      0.004   1.145
## E_Subject_s_income100_000___200_          0.117      0.202   0.582
## E_Subject_s_income20_000___100_0         -0.115      0.191  -0.604
## E_Subject_s_income20_000_shillin         -0.117      0.215  -0.547
## E_Subject_s_income200_000___500_         -0.058      0.194  -0.298
## E_Subject_s_income500_000___1_mi         -0.116      0.222  -0.521
## E_Subject_s_incomerefused_to_ans         -1.043      0.707  -1.475
## E_Can_you_read_001no__i_cannot_r          0.124      0.279   0.446
## E_Can_you_read_001yes__i_can_rea          0.079      0.131   0.600
## E_Can_you_read_001yes__i_can_som          0.031      0.115   0.267
## subjects.per.village                     -0.001      0.006  -0.092
## subcounty.blockingKanungu Town Council   -0.015      0.075  -0.205
## subcounty.blockingKayonza                 0.041      0.070   0.579
## subcounty.blockingKinaaba                 0.124      0.117   1.055
## subcounty.blockingKirima                  0.043      0.032   1.340
## subcounty.blockingKirundo                 0.245      0.079   3.103
## subcounty.blockingMpungu                  0.001      0.027   0.042
## subcounty.blockingNyabwishenya           -0.014      0.048  -0.296
## subcounty.blockingRuhija                 -0.060      0.028  -2.177
## subcounty.blockingSingles                 0.013      0.106   0.124
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.867
## b.satisfaction.b2                         0.005
## E_subject_s_gendermale                    0.708
## E_Subject_s_age                           0.252
## E_Subject_s_income100_000___200_          0.561
## E_Subject_s_income20_000___100_0          0.546
## E_Subject_s_income20_000_shillin          0.584
## E_Subject_s_income200_000___500_          0.766
## E_Subject_s_income500_000___1_mi          0.603
## E_Subject_s_incomerefused_to_ans          0.140
## E_Can_you_read_001no__i_cannot_r          0.656
## E_Can_you_read_001yes__i_can_rea          0.549
## E_Can_you_read_001yes__i_can_som          0.789
## subjects.per.village                      0.926
## subcounty.blockingKanungu Town Council    0.837
## subcounty.blockingKayonza                 0.563
## subcounty.blockingKinaaba                 0.291
## subcounty.blockingKirima                  0.180
## subcounty.blockingKirundo                 0.002
## subcounty.blockingMpungu                  0.966
## subcounty.blockingNyabwishenya            0.767
## subcounty.blockingRuhija                  0.029
## subcounty.blockingSingles                 0.901</code></pre>
<pre class="r"><code>nod.h10mgmt.spill &lt;- ols(e.satisfaction.b2 ~ treat + S1 + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h10mgmt.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.298      0.321  10.284
## treat                                     0.006      0.087   0.071
## S11                                      -0.036      0.126  -0.283
## S12                                      -0.049      0.145  -0.337
## b.satisfaction.b2                         0.171      0.058   2.936
## E_subject_s_gendermale                   -0.032      0.081  -0.394
## E_Subject_s_age                           0.004      0.003   1.154
## E_Subject_s_income100_000___200_          0.121      0.213   0.570
## E_Subject_s_income20_000___100_0         -0.109      0.206  -0.528
## E_Subject_s_income20_000_shillin         -0.112      0.218  -0.513
## E_Subject_s_income200_000___500_         -0.051      0.203  -0.250
## E_Subject_s_income500_000___1_mi         -0.114      0.218  -0.522
## E_Subject_s_incomerefused_to_ans         -1.042      0.708  -1.472
## E_Can_you_read_001no__i_cannot_r          0.120      0.278   0.431
## E_Can_you_read_001yes__i_can_rea          0.076      0.136   0.557
## E_Can_you_read_001yes__i_can_som          0.027      0.122   0.221
## subjects.per.village                     -0.001      0.006  -0.091
## subcounty.blockingKanungu Town Council   -0.005      0.122  -0.041
## subcounty.blockingKayonza                 0.039      0.070   0.551
## subcounty.blockingKinaaba                 0.100      0.173   0.578
## subcounty.blockingKirima                  0.043      0.062   0.700
## subcounty.blockingKirundo                 0.237      0.080   2.966
## subcounty.blockingMpungu                 -0.005      0.031  -0.158
## subcounty.blockingNyabwishenya           -0.012      0.048  -0.254
## subcounty.blockingRuhija                 -0.066      0.044  -1.498
## subcounty.blockingSingles                -0.008      0.086  -0.098
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.943
## S11                                       0.777
## S12                                       0.736
## b.satisfaction.b2                         0.003
## E_subject_s_gendermale                    0.694
## E_Subject_s_age                           0.248
## E_Subject_s_income100_000___200_          0.568
## E_Subject_s_income20_000___100_0          0.598
## E_Subject_s_income20_000_shillin          0.608
## E_Subject_s_income200_000___500_          0.802
## E_Subject_s_income500_000___1_mi          0.602
## E_Subject_s_incomerefused_to_ans          0.141
## E_Can_you_read_001no__i_cannot_r          0.667
## E_Can_you_read_001yes__i_can_rea          0.577
## E_Can_you_read_001yes__i_can_som          0.825
## subjects.per.village                      0.927
## subcounty.blockingKanungu Town Council    0.967
## subcounty.blockingKayonza                 0.581
## subcounty.blockingKinaaba                 0.564
## subcounty.blockingKirima                  0.484
## subcounty.blockingKirundo                 0.003
## subcounty.blockingMpungu                  0.874
## subcounty.blockingNyabwishenya            0.799
## subcounty.blockingRuhija                  0.134
## subcounty.blockingSingles                 0.922</code></pre>
<pre class="r"><code>nod.h10mgmt.spill2 &lt;- ols(e.satisfaction.b2 ~ treat + S2 + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h10mgmt.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.501      0.365   9.580
## treat                                    -0.002      0.089  -0.018
## S21                                      -0.365      0.389  -0.938
## S22                                      -0.204      0.379  -0.538
## S23                                      -0.303      0.384  -0.788
## S24                                      -0.313      0.338  -0.926
## b.satisfaction.b2                         0.177      0.056   3.150
## E_subject_s_gendermale                   -0.031      0.081  -0.388
## E_Subject_s_age                           0.004      0.004   1.127
## E_Subject_s_income100_000___200_          0.106      0.217   0.490
## E_Subject_s_income20_000___100_0         -0.127      0.202  -0.630
## E_Subject_s_income20_000_shillin         -0.129      0.226  -0.572
## E_Subject_s_income200_000___500_         -0.070      0.203  -0.344
## E_Subject_s_income500_000___1_mi         -0.138      0.225  -0.610
## E_Subject_s_incomerefused_to_ans         -1.057      0.666  -1.587
## E_Can_you_read_001no__i_cannot_r          0.141      0.281   0.501
## E_Can_you_read_001yes__i_can_rea          0.085      0.136   0.622
## E_Can_you_read_001yes__i_can_som          0.035      0.123   0.286
## subjects.per.village                     -0.001      0.007  -0.109
## subcounty.blockingKanungu Town Council    0.040      0.100   0.401
## subcounty.blockingKayonza                 0.059      0.068   0.866
## subcounty.blockingKinaaba                 0.122      0.132   0.922
## subcounty.blockingKirima                  0.076      0.035   2.178
## subcounty.blockingKirundo                 0.241      0.073   3.292
## subcounty.blockingMpungu                  0.040      0.019   2.128
## subcounty.blockingNyabwishenya            0.006      0.055   0.101
## subcounty.blockingRuhija                 -0.058      0.023  -2.546
## subcounty.blockingSingles                 0.027      0.118   0.231
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.986
## S21                                       0.348
## S22                                       0.591
## S23                                       0.431
## S24                                       0.354
## b.satisfaction.b2                         0.002
## E_subject_s_gendermale                    0.698
## E_Subject_s_age                           0.260
## E_Subject_s_income100_000___200_          0.624
## E_Subject_s_income20_000___100_0          0.528
## E_Subject_s_income20_000_shillin          0.568
## E_Subject_s_income200_000___500_          0.731
## E_Subject_s_income500_000___1_mi          0.542
## E_Subject_s_incomerefused_to_ans          0.113
## E_Can_you_read_001no__i_cannot_r          0.616
## E_Can_you_read_001yes__i_can_rea          0.534
## E_Can_you_read_001yes__i_can_som          0.775
## subjects.per.village                      0.913
## subcounty.blockingKanungu Town Council    0.689
## subcounty.blockingKayonza                 0.387
## subcounty.blockingKinaaba                 0.357
## subcounty.blockingKirima                  0.029
## subcounty.blockingKirundo                 0.001
## subcounty.blockingMpungu                  0.033
## subcounty.blockingNyabwishenya            0.919
## subcounty.blockingRuhija                  0.011
## subcounty.blockingSingles                 0.817</code></pre>
<pre class="r"><code>nod.h10mgmt.spill3 &lt;- ols(e.satisfaction.b2 ~ treat + S3 + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h10mgmt.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.448      0.368   9.373
## treat                                     0.021      0.088   0.243
## S32                                      -0.230      0.201  -1.148
## S33                                      -0.143      0.213  -0.671
## S34                                      -0.139      0.240  -0.579
## S35                                       0.034      0.431   0.078
## S36                                      -0.263      0.244  -1.080
## b.satisfaction.b2                         0.167      0.060   2.802
## E_subject_s_gendermale                   -0.032      0.076  -0.423
## E_Subject_s_age                           0.004      0.003   1.235
## E_Subject_s_income100_000___200_          0.103      0.203   0.508
## E_Subject_s_income20_000___100_0         -0.133      0.195  -0.683
## E_Subject_s_income20_000_shillin         -0.133      0.213  -0.625
## E_Subject_s_income200_000___500_         -0.079      0.198  -0.399
## E_Subject_s_income500_000___1_mi         -0.136      0.234  -0.579
## E_Subject_s_incomerefused_to_ans         -1.041      0.696  -1.495
## E_Can_you_read_001no__i_cannot_r          0.134      0.269   0.498
## E_Can_you_read_001yes__i_can_rea          0.082      0.144   0.569
## E_Can_you_read_001yes__i_can_som          0.035      0.123   0.283
## subjects.per.village                     -0.001      0.007  -0.108
## subcounty.blockingKanungu Town Council   -0.062      0.142  -0.434
## subcounty.blockingKayonza                 0.037      0.060   0.614
## subcounty.blockingKinaaba                 0.112      0.168   0.668
## subcounty.blockingKirima                 -0.003      0.087  -0.037
## subcounty.blockingKirundo                 0.265      0.075   3.532
## subcounty.blockingMpungu                 -0.009      0.026  -0.354
## subcounty.blockingNyabwishenya            0.003      0.058   0.055
## subcounty.blockingRuhija                 -0.050      0.018  -2.870
## subcounty.blockingSingles                -0.033      0.117  -0.285
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.808
## S32                                       0.251
## S33                                       0.502
## S34                                       0.563
## S35                                       0.938
## S36                                       0.280
## b.satisfaction.b2                         0.005
## E_subject_s_gendermale                    0.672
## E_Subject_s_age                           0.217
## E_Subject_s_income100_000___200_          0.611
## E_Subject_s_income20_000___100_0          0.494
## E_Subject_s_income20_000_shillin          0.532
## E_Subject_s_income200_000___500_          0.690
## E_Subject_s_income500_000___1_mi          0.562
## E_Subject_s_incomerefused_to_ans          0.135
## E_Can_you_read_001no__i_cannot_r          0.618
## E_Can_you_read_001yes__i_can_rea          0.569
## E_Can_you_read_001yes__i_can_som          0.777
## subjects.per.village                      0.914
## subcounty.blockingKanungu Town Council    0.664
## subcounty.blockingKayonza                 0.539
## subcounty.blockingKinaaba                 0.504
## subcounty.blockingKirima                  0.970
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.724
## subcounty.blockingNyabwishenya            0.956
## subcounty.blockingRuhija                  0.004
## subcounty.blockingSingles                 0.775</code></pre>
<pre class="r"><code>###H13: Support for Conservation (Importance of Protecting Bwindi)
nod.h13 &lt;- ols(e.conservation.b6 ~ treat + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h13</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.326      0.272  12.232
## treat                                     0.020      0.027   0.744
## b.conservation.b6                         0.126      0.070   1.801
## E_subject_s_gendermale                    0.029      0.010   2.764
## E_Subject_s_age                           0.001      0.001   0.504
## E_Subject_s_income100_000___200_         -0.034      0.013  -2.551
## E_Subject_s_income20_000___100_0         -0.070      0.027  -2.570
## E_Subject_s_income20_000_shillin         -0.122      0.032  -3.770
## E_Subject_s_income200_000___500_         -0.063      0.036  -1.747
## E_Subject_s_income500_000___1_mi         -0.014      0.020  -0.677
## E_Subject_s_incomerefused_to_ans         -0.200      0.146  -1.364
## E_Can_you_read_001no__i_cannot_r          0.020      0.040   0.495
## E_Can_you_read_001yes__i_can_rea          0.083      0.040   2.080
## E_Can_you_read_001yes__i_can_som          0.036      0.062   0.585
## subjects.per.village                      0.000      0.001   0.120
## subcounty.blockingKanungu Town Council    0.052      0.009   5.607
## subcounty.blockingKayonza                 0.058      0.006   9.023
## subcounty.blockingKinaaba                 0.119      0.017   7.158
## subcounty.blockingKirima                  0.058      0.005  11.990
## subcounty.blockingKirundo                -0.009      0.007  -1.196
## subcounty.blockingMpungu                  0.014      0.006   2.542
## subcounty.blockingNyabwishenya            0.079      0.006  12.621
## subcounty.blockingRuhija                  0.070      0.003  23.818
## subcounty.blockingSingles                 0.068      0.012   5.549
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.457
## b.conservation.b6                         0.072
## E_subject_s_gendermale                    0.006
## E_Subject_s_age                           0.614
## E_Subject_s_income100_000___200_          0.011
## E_Subject_s_income20_000___100_0          0.010
## E_Subject_s_income20_000_shillin          0.000
## E_Subject_s_income200_000___500_          0.081
## E_Subject_s_income500_000___1_mi          0.498
## E_Subject_s_incomerefused_to_ans          0.173
## E_Can_you_read_001no__i_cannot_r          0.621
## E_Can_you_read_001yes__i_can_rea          0.038
## E_Can_you_read_001yes__i_can_som          0.558
## subjects.per.village                      0.904
## subcounty.blockingKanungu Town Council    0.000
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.232
## subcounty.blockingMpungu                  0.011
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>nod.h13.spill &lt;- ols(e.conservation.b6 ~ treat + S1 + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h13.spill</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.311      0.266  12.469
## treat                                     0.024      0.030   0.814
## S11                                       0.027      0.053   0.507
## S12                                       0.024      0.039   0.619
## b.conservation.b6                         0.124      0.071   1.744
## E_subject_s_gendermale                    0.030      0.009   3.200
## E_Subject_s_age                           0.001      0.001   0.501
## E_Subject_s_income100_000___200_         -0.040      0.028  -1.418
## E_Subject_s_income20_000___100_0         -0.076      0.035  -2.210
## E_Subject_s_income20_000_shillin         -0.127      0.044  -2.864
## E_Subject_s_income200_000___500_         -0.070      0.039  -1.803
## E_Subject_s_income500_000___1_mi         -0.017      0.024  -0.700
## E_Subject_s_incomerefused_to_ans         -0.199      0.139  -1.434
## E_Can_you_read_001no__i_cannot_r          0.022      0.042   0.532
## E_Can_you_read_001yes__i_can_rea          0.084      0.038   2.247
## E_Can_you_read_001yes__i_can_som          0.038      0.059   0.639
## subjects.per.village                      0.000      0.001   0.029
## subcounty.blockingKanungu Town Council    0.051      0.015   3.357
## subcounty.blockingKayonza                 0.059      0.007   8.461
## subcounty.blockingKinaaba                 0.136      0.043   3.141
## subcounty.blockingKirima                  0.061      0.011   5.651
## subcounty.blockingKirundo                -0.003      0.013  -0.236
## subcounty.blockingMpungu                  0.018      0.010   1.840
## subcounty.blockingNyabwishenya            0.076      0.012   6.216
## subcounty.blockingRuhija                  0.075      0.013   5.986
## subcounty.blockingSingles                 0.081      0.021   3.911
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.415
## S11                                       0.612
## S12                                       0.536
## b.conservation.b6                         0.081
## E_subject_s_gendermale                    0.001
## E_Subject_s_age                           0.617
## E_Subject_s_income100_000___200_          0.156
## E_Subject_s_income20_000___100_0          0.027
## E_Subject_s_income20_000_shillin          0.004
## E_Subject_s_income200_000___500_          0.071
## E_Subject_s_income500_000___1_mi          0.484
## E_Subject_s_incomerefused_to_ans          0.151
## E_Can_you_read_001no__i_cannot_r          0.595
## E_Can_you_read_001yes__i_can_rea          0.025
## E_Can_you_read_001yes__i_can_som          0.523
## subjects.per.village                      0.977
## subcounty.blockingKanungu Town Council    0.001
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.002
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.814
## subcounty.blockingMpungu                  0.066
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>nod.h13.spill2 &lt;- ols(e.conservation.b6 ~ treat + S2 + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h13.spill2</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.421      0.255  13.431
## treat                                     0.014      0.025   0.558
## S21                                      -0.118      0.038  -3.074
## S22                                      -0.094      0.036  -2.622
## S23                                      -0.117      0.042  -2.784
## S24                                      -0.108      0.055  -1.963
## b.conservation.b6                         0.127      0.071   1.776
## E_subject_s_gendermale                    0.028      0.010   2.916
## E_Subject_s_age                           0.001      0.001   0.534
## E_Subject_s_income100_000___200_         -0.035      0.019  -1.892
## E_Subject_s_income20_000___100_0         -0.071      0.031  -2.297
## E_Subject_s_income20_000_shillin         -0.123      0.038  -3.264
## E_Subject_s_income200_000___500_         -0.065      0.037  -1.745
## E_Subject_s_income500_000___1_mi         -0.021      0.022  -0.952
## E_Subject_s_incomerefused_to_ans         -0.203      0.141  -1.441
## E_Can_you_read_001no__i_cannot_r          0.024      0.040   0.599
## E_Can_you_read_001yes__i_can_rea          0.085      0.040   2.122
## E_Can_you_read_001yes__i_can_som          0.037      0.060   0.619
## subjects.per.village                      0.000      0.001   0.284
## subcounty.blockingKanungu Town Council    0.067      0.021   3.127
## subcounty.blockingKayonza                 0.060      0.009   6.479
## subcounty.blockingKinaaba                 0.116      0.022   5.289
## subcounty.blockingKirima                  0.067      0.006  11.760
## subcounty.blockingKirundo                -0.007      0.009  -0.766
## subcounty.blockingMpungu                  0.022      0.009   2.378
## subcounty.blockingNyabwishenya            0.087      0.008  10.766
## subcounty.blockingRuhija                  0.072      0.005  14.526
## subcounty.blockingSingles                 0.061      0.017   3.667
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.577
## S21                                       0.002
## S22                                       0.009
## S23                                       0.005
## S24                                       0.050
## b.conservation.b6                         0.076
## E_subject_s_gendermale                    0.004
## E_Subject_s_age                           0.593
## E_Subject_s_income100_000___200_          0.059
## E_Subject_s_income20_000___100_0          0.022
## E_Subject_s_income20_000_shillin          0.001
## E_Subject_s_income200_000___500_          0.081
## E_Subject_s_income500_000___1_mi          0.341
## E_Subject_s_incomerefused_to_ans          0.150
## E_Can_you_read_001no__i_cannot_r          0.549
## E_Can_you_read_001yes__i_can_rea          0.034
## E_Can_you_read_001yes__i_can_som          0.536
## subjects.per.village                      0.777
## subcounty.blockingKanungu Town Council    0.002
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.443
## subcounty.blockingMpungu                  0.017
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.000</code></pre>
<pre class="r"><code>nod.h13.spill3 &lt;- ols(e.conservation.b6 ~ treat + S3 + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking, data=M, cluster=&quot;subcounty.blocking&quot;)
nod.h13.spill3</code></pre>
<pre><code>##                                        Estimate Std. Error t value
## (Intercept)                               3.386      0.275  12.302
## treat                                     0.010      0.023   0.436
## S32                                      -0.033      0.037  -0.905
## S33                                      -0.097      0.052  -1.880
## S34                                      -0.064      0.040  -1.621
## S35                                      -0.085      0.057  -1.488
## S36                                      -0.124      0.043  -2.859
## b.conservation.b6                         0.135      0.069   1.958
## E_subject_s_gendermale                    0.025      0.010   2.434
## E_Subject_s_age                           0.001      0.001   0.487
## E_Subject_s_income100_000___200_         -0.035      0.020  -1.744
## E_Subject_s_income20_000___100_0         -0.071      0.032  -2.191
## E_Subject_s_income20_000_shillin         -0.120      0.038  -3.168
## E_Subject_s_income200_000___500_         -0.064      0.041  -1.557
## E_Subject_s_income500_000___1_mi         -0.011      0.025  -0.443
## E_Subject_s_incomerefused_to_ans         -0.211      0.149  -1.418
## E_Can_you_read_001no__i_cannot_r          0.017      0.042   0.409
## E_Can_you_read_001yes__i_can_rea          0.082      0.037   2.210
## E_Can_you_read_001yes__i_can_som          0.033      0.060   0.545
## subjects.per.village                      0.000      0.001  -0.039
## subcounty.blockingKanungu Town Council    0.037      0.031   1.219
## subcounty.blockingKayonza                 0.047      0.007   6.990
## subcounty.blockingKinaaba                 0.131      0.024   5.475
## subcounty.blockingKirima                  0.056      0.007   8.182
## subcounty.blockingKirundo                -0.031      0.007  -4.446
## subcounty.blockingMpungu                 -0.001      0.007  -0.110
## subcounty.blockingNyabwishenya            0.051      0.013   4.077
## subcounty.blockingRuhija                  0.059      0.004  15.039
## subcounty.blockingSingles                 0.003      0.030   0.094
##                                        Pr(&gt;|t|)
## (Intercept)                               0.000
## treat                                     0.663
## S32                                       0.366
## S33                                       0.060
## S34                                       0.105
## S35                                       0.137
## S36                                       0.004
## b.conservation.b6                         0.050
## E_subject_s_gendermale                    0.015
## E_Subject_s_age                           0.626
## E_Subject_s_income100_000___200_          0.081
## E_Subject_s_income20_000___100_0          0.028
## E_Subject_s_income20_000_shillin          0.002
## E_Subject_s_income200_000___500_          0.119
## E_Subject_s_income500_000___1_mi          0.658
## E_Subject_s_incomerefused_to_ans          0.156
## E_Can_you_read_001no__i_cannot_r          0.682
## E_Can_you_read_001yes__i_can_rea          0.027
## E_Can_you_read_001yes__i_can_som          0.586
## subjects.per.village                      0.969
## subcounty.blockingKanungu Town Council    0.223
## subcounty.blockingKayonza                 0.000
## subcounty.blockingKinaaba                 0.000
## subcounty.blockingKirima                  0.000
## subcounty.blockingKirundo                 0.000
## subcounty.blockingMpungu                  0.912
## subcounty.blockingNyabwishenya            0.000
## subcounty.blockingRuhija                  0.000
## subcounty.blockingSingles                 0.925</code></pre>
<pre class="r"><code>######################################################################################
###Table E1. Attrition in endline survey by treatment condition
######################################################################################

table(M$treat,M$attr)</code></pre>
<pre><code>##    
##       0   1
##   0 534 341
##   1 465 332</code></pre>
<pre class="r"><code>prop.table(table(M$treat,M$attr),1)</code></pre>
<pre><code>##    
##             0         1
##   0 0.6102857 0.3897143
##   1 0.5834379 0.4165621</code></pre>
<pre class="r"><code>chisq.test(table(M$treat,M$attr))</code></pre>
<pre><code>## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(M$treat, M$attr)
## X-squared = 1.1409, df = 1, p-value = 0.2855</code></pre>
<pre class="r"><code>######################################################################################
###Analysis of Attrition reported in-text, SI Section E
######################################################################################
source('outcome_var_setup.R') #Setting up baseline covariates without imputation

#####Analysis on differential missingness reported in text, but not in the table
M.attr &lt;- subset(M, select=c(&quot;attr&quot;,&quot;treat&quot;,&quot;b.knowledge.b7&quot;,&quot;b.knowledge.b8&quot;,&quot;b.knowledge.b9&quot;,&quot;b.participate.b10&quot;,&quot;b.participate.b11&quot;,&quot;b.participate.b12&quot;,&quot;b.satisfaction.b2&quot;,&quot;b.satisfaction.b3&quot;,&quot;b.conservation.b6&quot;,&quot;subjects.per.village&quot;))
#Note: participation in revenue-sharing meeting is only post-treatment, not included here

#No missingness in any of the main variables
table(M.attr$treat,useNA = &quot;always&quot;)</code></pre>
<pre><code>## 
##    0    1 &lt;NA&gt; 
##  875  797    0</code></pre>
<pre class="r"><code>table(M.attr$attr,useNA = &quot;always&quot;)</code></pre>
<pre><code>## 
##    0    1 &lt;NA&gt; 
##  999  673    0</code></pre>
<pre class="r"><code>table(M.attr$subjects.per.village,useNA = &quot;always&quot;)</code></pre>
<pre><code>## 
##    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20 
##    6   21   24   18   50   66  108   78   84   60  144   85   36   76   40 
##   22   24   26   27   28   29   31   32   33   34   36   38   40   42   45 
##   44   24   78   27   84   29   31   64   33   34   72   76   40   42   45 
##   53 &lt;NA&gt; 
##   53    0</code></pre>
<pre class="r"><code>M.attr[is.na(M.attr)] &lt;- &quot;missing&quot;

##F-Test on subgroup differences in attrition by baseline survey response
attr.mod &lt;- lm(attr ~ treat + b.knowledge.b7 + b.knowledge.b8 + b.knowledge.b9 + b.participate.b10 + b.participate.b11 + b.participate.b12 + b.satisfaction.b2 + b.satisfaction.b3 + b.conservation.b6 + subjects.per.village, data=M.attr)
summary(attr.mod)</code></pre>
<pre><code>## 
## Call:
## lm(formula = attr ~ treat + b.knowledge.b7 + b.knowledge.b8 + 
##     b.knowledge.b9 + b.participate.b10 + b.participate.b11 + 
##     b.participate.b12 + b.satisfaction.b2 + b.satisfaction.b3 + 
##     b.conservation.b6 + subjects.per.village, data = M.attr)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6429 -0.3177 -0.2181  0.4066  0.9631 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(&gt;|t|)  
## (Intercept)              -0.1775901  0.3103079  -0.572   0.5672  
## treat                     0.0349939  0.0233060   1.501   0.1334  
## b.knowledge.b72          -0.0868351  0.1142366  -0.760   0.4473  
## b.knowledge.b73          -0.1091490  0.1198352  -0.911   0.3625  
## b.knowledge.b74          -0.1124999  0.1092722  -1.030   0.3034  
## b.knowledge.b75          -0.0769748  0.1126305  -0.683   0.4944  
## b.knowledge.b7missing    -0.0089720  0.3816413  -0.024   0.9812  
## b.knowledge.b82           0.0082879  0.0946001   0.088   0.9302  
## b.knowledge.b83           0.0640750  0.0815128   0.786   0.4319  
## b.knowledge.b84           0.1029370  0.0824546   1.248   0.2121  
## b.knowledge.b8missing     0.2832667  0.1657398   1.709   0.0876 .
## b.knowledge.b92          -0.0404426  0.1015110  -0.398   0.6904  
## b.knowledge.b93          -0.0556166  0.0938241  -0.593   0.5534  
## b.knowledge.b94          -0.0742563  0.0956061  -0.777   0.4375  
## b.knowledge.b9missing    -0.1213158  0.2295053  -0.529   0.5972  
## b.participate.b102       -0.0216695  0.0648541  -0.334   0.7383  
## b.participate.b103        0.0653619  0.0589470   1.109   0.2677  
## b.participate.b104        0.0360913  0.0539470   0.669   0.5036  
## b.participate.b10missing  0.0704819  0.2144774   0.329   0.7425  
## b.participate.b112       -0.0289444  0.0927673  -0.312   0.7551  
## b.participate.b113        0.0114435  0.0939885   0.122   0.9031  
## b.participate.b114        0.0073005  0.0888615   0.082   0.9345  
## b.participate.b115       -0.0663956  0.0912948  -0.727   0.4672  
## b.participate.b11missing -0.1409923  0.2943398  -0.479   0.6320  
## b.participate.b122        0.0865923  0.0917131   0.944   0.3452  
## b.participate.b123        0.0269612  0.0687329   0.392   0.6949  
## b.participate.b124        0.0208179  0.0583772   0.357   0.7214  
## b.participate.b12missing  0.1447400  0.1240162   1.167   0.2433  
## b.satisfaction.b22        0.0685993  0.1220311   0.562   0.5741  
## b.satisfaction.b23        0.0503638  0.1796184   0.280   0.7792  
## b.satisfaction.b24        0.1089818  0.1051382   1.037   0.3001  
## b.satisfaction.b25        0.1415219  0.1039137   1.362   0.1734  
## b.satisfaction.b2missing -0.1368189  0.3484783  -0.393   0.6947  
## b.satisfaction.b32        0.0728101  0.1222795   0.595   0.5516  
## b.satisfaction.b33        0.2188298  0.1588760   1.377   0.1686  
## b.satisfaction.b34        0.1353466  0.1180283   1.147   0.2517  
## b.satisfaction.b35        0.0906338  0.1192749   0.760   0.4474  
## b.satisfaction.b3missing  0.0677403  0.2608730   0.260   0.7952  
## b.conservation.b62        0.2713183  0.3010984   0.901   0.3677  
## b.conservation.b63        0.3142927  0.2845723   1.104   0.2696  
## b.conservation.b64        0.2243523  0.2809288   0.799   0.4246  
## b.conservation.b6missing  0.5889440  0.6624853   0.889   0.3741  
## subjects.per.village      0.0008544  0.0010066   0.849   0.3961  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4655 on 1629 degrees of freedom
## Multiple R-squared:  0.1222, Adjusted R-squared:  0.09955 
## F-statistic: 5.399 on 42 and 1629 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>attr.mod.int &lt;- lm(attr ~ treat*b.knowledge.b7 + treat*b.knowledge.b8 + treat*b.knowledge.b9 + treat*b.participate.b10 + treat*b.participate.b11 + treat*b.participate.b12 + treat*b.satisfaction.b2 + treat*b.satisfaction.b3 + treat*b.conservation.b6 + treat*subjects.per.village, data=M.attr)
summary(attr.mod.int)</code></pre>
<pre><code>## 
## Call:
## lm(formula = attr ~ treat * b.knowledge.b7 + treat * b.knowledge.b8 + 
##     treat * b.knowledge.b9 + treat * b.participate.b10 + treat * 
##     b.participate.b11 + treat * b.participate.b12 + treat * b.satisfaction.b2 + 
##     treat * b.satisfaction.b3 + treat * b.conservation.b6 + treat * 
##     subjects.per.village, data = M.attr)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7225 -0.3337 -0.1806  0.3970  1.1346 
## 
## Coefficients: (1 not defined because of singularities)
##                                  Estimate Std. Error t value Pr(&gt;|t|)  
## (Intercept)                    -0.3321586  0.5725482  -0.580   0.5619  
## treat                           0.1078920  0.6909092   0.156   0.8759  
## b.knowledge.b72                -0.1475428  0.1732461  -0.852   0.3945  
## b.knowledge.b73                -0.0492867  0.1789074  -0.275   0.7830  
## b.knowledge.b74                -0.1038661  0.1675555  -0.620   0.5354  
## b.knowledge.b75                -0.0938692  0.1705659  -0.550   0.5822  
## b.knowledge.b7missing           1.8122028  1.4273522   1.270   0.2044  
## b.knowledge.b82                 0.1290612  0.1845327   0.699   0.4844  
## b.knowledge.b83                 0.2244766  0.1766644   1.271   0.2040  
## b.knowledge.b84                 0.2272864  0.1776662   1.279   0.2010  
## b.knowledge.b8missing           0.6516838  0.3279111   1.987   0.0471 *
## b.knowledge.b92                -0.0888347  0.1740474  -0.510   0.6098  
## b.knowledge.b93                -0.0460130  0.1672466  -0.275   0.7833  
## b.knowledge.b94                -0.0847498  0.1705947  -0.497   0.6194  
## b.knowledge.b9missing          -0.8015942  0.7080665  -1.132   0.2578  
## b.participate.b102             -0.0448911  0.0947193  -0.474   0.6356  
## b.participate.b103             -0.0192413  0.0851707  -0.226   0.8213  
## b.participate.b104              0.0482041  0.0795966   0.606   0.5449  
## b.participate.b10missing        0.0199216  0.4004390   0.050   0.9603  
## b.participate.b112             -0.0481532  0.1547564  -0.311   0.7557  
## b.participate.b113              0.0899393  0.1490070   0.604   0.5462  
## b.participate.b114              0.0114236  0.1447633   0.079   0.9371  
## b.participate.b115             -0.0392639  0.1461859  -0.269   0.7883  
## b.participate.b11missing       -0.0665986  0.5097702  -0.131   0.8961  
## b.participate.b122              0.0483497  0.1264196   0.382   0.7022  
## b.participate.b123              0.0709457  0.1022743   0.694   0.4880  
## b.participate.b124              0.0049243  0.0882046   0.056   0.9555  
## b.participate.b12missing       -0.0134119  0.1836247  -0.073   0.9418  
## b.satisfaction.b22              0.0708019  0.1759487   0.402   0.6874  
## b.satisfaction.b23             -0.0995409  0.2203712  -0.452   0.6515  
## b.satisfaction.b24              0.0548260  0.1495928   0.367   0.7140  
## b.satisfaction.b25              0.0923058  0.1492165   0.619   0.5363  
## b.satisfaction.b2missing       -0.1474893  0.4933964  -0.299   0.7650  
## b.satisfaction.b32              0.0107890  0.2155120   0.050   0.9601  
## b.satisfaction.b33              0.3435891  0.2861240   1.201   0.2300  
## b.satisfaction.b34              0.0387669  0.2071160   0.187   0.8515  
## b.satisfaction.b35             -0.0661476  0.2091326  -0.316   0.7518  
## b.satisfaction.b3missing       -0.6754361  0.5868112  -1.151   0.2499  
## b.conservation.b62              0.5936353  0.5092891   1.166   0.2439  
## b.conservation.b63              0.4893658  0.4847788   1.009   0.3129  
## b.conservation.b64              0.4299486  0.4776768   0.900   0.3682  
## b.conservation.b6missing        0.1655704  0.8547372   0.194   0.8464  
## subjects.per.village            0.0002627  0.0014052   0.187   0.8517  
## treat:b.knowledge.b72           0.1123900  0.2361715   0.476   0.6342  
## treat:b.knowledge.b73          -0.1242888  0.2483086  -0.501   0.6168  
## treat:b.knowledge.b74          -0.0150358  0.2243678  -0.067   0.9466  
## treat:b.knowledge.b75           0.0671608  0.2311842   0.291   0.7715  
## treat:b.knowledge.b7missing    -1.9520115  1.4122589  -1.382   0.1671  
## treat:b.knowledge.b82          -0.1145703  0.2240528  -0.511   0.6092  
## treat:b.knowledge.b83          -0.2032207  0.2021228  -1.005   0.3148  
## treat:b.knowledge.b84          -0.1061038  0.2047685  -0.518   0.6044  
## treat:b.knowledge.b8missing    -0.4490756  0.3861332  -1.163   0.2450  
## treat:b.knowledge.b92           0.0758445  0.2217422   0.342   0.7324  
## treat:b.knowledge.b93          -0.0576029  0.2056071  -0.280   0.7794  
## treat:b.knowledge.b94          -0.0494757  0.2098074  -0.236   0.8136  
## treat:b.knowledge.b9missing     0.7240426  0.7554964   0.958   0.3380  
## treat:b.participate.b102        0.0409784  0.1331817   0.308   0.7584  
## treat:b.participate.b103        0.1987023  0.1213150   1.638   0.1016  
## treat:b.participate.b104       -0.0103334  0.1129124  -0.092   0.9271  
## treat:b.participate.b10missing  0.4365927  0.4976704   0.877   0.3805  
## treat:b.participate.b112        0.0667533  0.1990670   0.335   0.7374  
## treat:b.participate.b113       -0.1350918  0.2020169  -0.669   0.5038  
## treat:b.participate.b114        0.0268603  0.1877735   0.143   0.8863  
## treat:b.participate.b115       -0.0426048  0.1928541  -0.221   0.8252  
## treat:b.participate.b11missing -0.0716984  0.6350423  -0.113   0.9101  
## treat:b.participate.b122        0.1423508  0.1950393   0.730   0.4656  
## treat:b.participate.b123       -0.0921439  0.1419406  -0.649   0.5163  
## treat:b.participate.b124        0.0307235  0.1207046   0.255   0.7991  
## treat:b.participate.b12missing  0.3229175  0.2644867   1.221   0.2223  
## treat:b.satisfaction.b22        0.0013464  0.2629528   0.005   0.9959  
## treat:b.satisfaction.b23        0.5838192  0.4465753   1.307   0.1913  
## treat:b.satisfaction.b24        0.1266117  0.2275978   0.556   0.5781  
## treat:b.satisfaction.b25        0.1119427  0.2256582   0.496   0.6199  
## treat:b.satisfaction.b2missing -0.0410845  0.7106964  -0.058   0.9539  
## treat:b.satisfaction.b32        0.1015746  0.2675616   0.380   0.7043  
## treat:b.satisfaction.b33       -0.1438273  0.3527580  -0.408   0.6835  
## treat:b.satisfaction.b34        0.1645344  0.2574413   0.639   0.5228  
## treat:b.satisfaction.b35        0.2897598  0.2602934   1.113   0.2658  
## treat:b.satisfaction.b3missing  0.8727129  0.6601191   1.322   0.1863  
## treat:b.conservation.b62       -0.5311075  0.6524135  -0.814   0.4157  
## treat:b.conservation.b63       -0.2178400  0.6218139  -0.350   0.7261  
## treat:b.conservation.b64       -0.2805655  0.6111199  -0.459   0.6462  
## treat:b.conservation.b6missing         NA         NA      NA       NA  
## treat:subjects.per.village      0.0009155  0.0020505   0.447   0.6553  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4655 on 1589 degrees of freedom
## Multiple R-squared:  0.1436, Adjusted R-squared:  0.09944 
## F-statistic:  3.25 on 82 and 1589 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>anova(attr.mod.int,attr.mod) #No indication of differential attrition inconsistent with random chance</code></pre>
<pre><code>## Analysis of Variance Table
## 
## Model 1: attr ~ treat * b.knowledge.b7 + treat * b.knowledge.b8 + treat * 
##     b.knowledge.b9 + treat * b.participate.b10 + treat * b.participate.b11 + 
##     treat * b.participate.b12 + treat * b.satisfaction.b2 + treat * 
##     b.satisfaction.b3 + treat * b.conservation.b6 + treat * subjects.per.village
## Model 2: attr ~ treat + b.knowledge.b7 + b.knowledge.b8 + b.knowledge.b9 + 
##     b.participate.b10 + b.participate.b11 + b.participate.b12 + 
##     b.satisfaction.b2 + b.satisfaction.b3 + b.conservation.b6 + 
##     subjects.per.village
##   Res.Df    RSS  Df Sum of Sq     F Pr(&gt;F)
## 1   1589 344.35                           
## 2   1629 352.98 -40   -8.6249 0.995 0.4802</code></pre>
<pre class="r"><code>######################################################################################
###Table E2. Balance on missingness in pre-treatment measures before and after endline attrition
######################################################################################
M.attr &lt;- subset(M, select=c(&quot;attr&quot;,&quot;treat&quot;,&quot;b.knowledge.b7&quot;,&quot;b.knowledge.b8&quot;,&quot;b.knowledge.b9&quot;,&quot;b.participate.b10&quot;,&quot;b.participate.b11&quot;,&quot;b.participate.b12&quot;,&quot;b.satisfaction.b2&quot;,&quot;b.satisfaction.b3&quot;,&quot;b.conservation.b6&quot;,&quot;subjects.per.village&quot;))
M.attr[is.na(M.attr)] &lt;- 0
M.attr[M.attr&gt;0] &lt;- 1 #All covariates values transformed to indicator of missinging

###H7.2: Know How RS Works Generally
1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==1]) / length(M.attr$b.knowledge.b8[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3839398</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b8[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==1]) / length(M.attr$b.knowledge.b8[M.attr$treat==1]))) / 
     length(M.attr$b.knowledge.b8[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01716418</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3954286</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b8[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==0]))) / 
     length(M.attr$b.knowledge.b8[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01656108</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2709677</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02058115</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2490637</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01906538</code></pre>
<pre class="r"><code>###H7.3: How RS Works in my Village
1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==1]) / length(M.attr$b.knowledge.b9[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.378921</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b9[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==1]) / length(M.attr$b.knowledge.b9[M.attr$treat==1]))) / 
     length(M.attr$b.knowledge.b9[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01705881</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3908571</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b9[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==0]))) / 
     length(M.attr$b.knowledge.b9[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01648083</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2688172</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02055987</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2453184</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01864174</code></pre>
<pre class="r"><code>###H9.2: Know Right Person to Contact
1 - (sum(M.attr$b.participate.b12[M.attr$treat==1]) / length(M.attr$b.participate.b12[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3801757</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b12[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.participate.b12[M.attr$treat==1]) / length(M.attr$b.participate.b12[M.attr$treat==1]))) / 
     length(M.attr$b.participate.b12[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01717392</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b12[M.attr$treat==0]) / length(M.attr$b.participate.b12[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.4022857</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b12[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.participate.b12[M.attr$treat==0]) / length(M.attr$b.participate.b12[M.attr$treat==0]))) / 
     length(M.attr$b.participate.b12[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.0166663</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2666667</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b12[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.0201625</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.258427</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b12[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01896885</code></pre>
<pre class="r"><code>###H9.1: Satisfaction with opportunities to communicate with UWA about RS
1 - (sum(M.attr$b.participate.b11[M.attr$treat==1]) / length(M.attr$b.participate.b11[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3726474</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b11[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.participate.b11[M.attr$treat==1]) / length(M.attr$b.participate.b11[M.attr$treat==1]))) / 
     length(M.attr$b.participate.b11[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.0171879</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b11[M.attr$treat==0]) / length(M.attr$b.participate.b11[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3908571</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b11[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.participate.b11[M.attr$treat==0]) / length(M.attr$b.participate.b11[M.attr$treat==0]))) / 
     length(M.attr$b.participate.b11[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01647129</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2645161</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b11[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02035834</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2453184</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b11[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01844262</code></pre>
<pre class="r"><code>###H8.1: Opportunities to Participate in Planning
1 - (sum(M.attr$b.participate.b10[M.attr$treat==1]) / length(M.attr$b.participate.b10[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3739021</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b10[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.participate.b10[M.attr$treat==1]) / length(M.attr$b.participate.b10[M.attr$treat==1]))) / 
     length(M.attr$b.participate.b10[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01717221</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b10[M.attr$treat==0]) / length(M.attr$b.participate.b10[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3931429</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b10[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.participate.b10[M.attr$treat==0]) / length(M.attr$b.participate.b10[M.attr$treat==0]))) / 
     length(M.attr$b.participate.b10[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01658934</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2623656</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b10[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02047609</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2490637</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.participate.b10[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01856222</code></pre>
<pre class="r"><code>###H7.1: Satisfaction with RS Info from UWA
1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==1]) / length(M.attr$b.knowledge.b7[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3726474</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b7[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==1]) / length(M.attr$b.knowledge.b7[M.attr$treat==1]))) / 
     length(M.attr$b.knowledge.b7[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01709988</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3897143</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b7[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==0]))) / 
     length(M.attr$b.knowledge.b7[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01628802</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2623656</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02044331</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2434457</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01874928</code></pre>
<pre class="r"><code>###H11: Satisfaction with RS
1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==1]) / length(M.attr$b.satisfaction.b3[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3751568</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b3[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==1]) / length(M.attr$b.satisfaction.b3[M.attr$treat==1]))) / 
     length(M.attr$b.satisfaction.b3[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01706145</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3908571</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b3[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==0]))) / 
     length(M.attr$b.satisfaction.b3[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01662647</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2645161</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02022033</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2453184</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01893935</code></pre>
<pre class="r"><code>###H10: Satisfaction with Park Management
1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==1]) / length(M.attr$b.satisfaction.b2[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.3713927</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b2[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==1]) / length(M.attr$b.satisfaction.b2[M.attr$treat==1]))) / 
     length(M.attr$b.satisfaction.b2[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.0171277</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3908571</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b2[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==0]))) / 
     length(M.attr$b.satisfaction.b2[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01655845</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2623656</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.02019925</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2453184</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01879725</code></pre>
<pre class="r"><code>###H13: Support for Conservation (Importance of Protecting Bwindi)
1 - (sum(M.attr$b.conservation.b6[M.attr$treat==1]) / length(M.attr$b.conservation.b6[M.attr$treat==1]))</code></pre>
<pre><code>## [1] 0.370138</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.conservation.b6[M.attr$treat==1]),
          prob= 1 - (sum(M.attr$b.conservation.b6[M.attr$treat==1]) / length(M.attr$b.conservation.b6[M.attr$treat==1]))) / 
     length(M.attr$b.conservation.b6[M.attr$treat==1])
)</code></pre>
<pre><code>## [1] 0.01702882</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.conservation.b6[M.attr$treat==0]) / length(M.attr$b.conservation.b6[M.attr$treat==0]))</code></pre>
<pre><code>## [1] 0.3897143</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.conservation.b6[M.attr$treat==0]),
          prob= 1 - (sum(M.attr$b.conservation.b6[M.attr$treat==0]) / length(M.attr$b.conservation.b6[M.attr$treat==0]))) / 
     length(M.attr$b.conservation.b6[M.attr$treat==0])
)</code></pre>
<pre><code>## [1] 0.01665996</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2602151</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0]) / length(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.0202676</code></pre>
<pre class="r"><code>1 - (sum(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0]))</code></pre>
<pre><code>## [1] 0.2434457</code></pre>
<pre class="r"><code>sd(rbinom(n= 10000,
          size= length(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0]),
          prob= 1 - (sum(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0]) / length(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0]))) / 
     length(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M.attr$attr==0])
)</code></pre>
<pre><code>## [1] 0.01866321</code></pre>
<pre class="r"><code>######################################################################################
###Table E3. Balance on values of pre-treatment measures, conditional on non-missingness
######################################################################################
source('outcome_var_setup.R') #Setting up baseline covariates without imputation

M.attr &lt;- subset(M, select=c(&quot;attr&quot;,&quot;treat&quot;,&quot;b.knowledge.b7&quot;,&quot;b.knowledge.b8&quot;,&quot;b.knowledge.b9&quot;,&quot;b.participate.b10&quot;,&quot;b.participate.b11&quot;,&quot;b.participate.b12&quot;,&quot;b.satisfaction.b2&quot;,&quot;b.satisfaction.b3&quot;,&quot;b.conservation.b6&quot;,&quot;subjects.per.village&quot;))
boot.se &lt;- function(vector,sims=10000){
  store &lt;&lt;- rep(NA, sims)
  for (i in 1:sims){
    store[i] &lt;- mean(sample(vector, replace=T), na.rm=T)
  }
  return(sd(store))
}

###H7.2: Know How RS Works Generally
mean(M.attr$b.knowledge.b8[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.342159</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b8[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.04189486</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b8[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.500945</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b8[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.03056609</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.300885</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b8[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05129842</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.481297</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b8[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.03645434</code></pre>
<pre class="r"><code>###H7.3: How RS Works in my Village
mean(M.attr$b.knowledge.b9[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.426263</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b9[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.03783376</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b9[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.534709</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b9[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.03149328</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.417647</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b9[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.04579599</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.533499</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b9[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.03662742</code></pre>
<pre class="r"><code>###H9.2: Know Right Person to Contact
mean(M.attr$b.participate.b12[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.489879</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b12[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.04375198</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b12[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.552581</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b12[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.03892014</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b12[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.472141</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b12[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05357858</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b12[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.560606</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b12[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.04505151</code></pre>
<pre class="r"><code>###H9.1: Satisfaction with opportunities to communicate with UWA about RS
mean(M.attr$b.participate.b11[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.838</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b11[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.05194951</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b11[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.851782</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b11[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.04938628</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b11[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.830409</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b11[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.06433233</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b11[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.866005</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b11[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05708791</code></pre>
<pre class="r"><code>###H8.1: Opportunities to Participate in Planning
mean(M.attr$b.participate.b10[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.226453</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b10[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.04664911</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b10[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.269303</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b10[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.04473322</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b10[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.204082</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b10[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05876823</code></pre>
<pre class="r"><code>mean(M.attr$b.participate.b10[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.264339</code></pre>
<pre class="r"><code>boot.se(M.attr$b.participate.b10[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05129646</code></pre>
<pre class="r"><code>###H7.1: Satisfaction with RS Info from UWA
mean(M.attr$b.knowledge.b7[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 4.016</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b7[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.04608382</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b7[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.013109</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b7[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.04530983</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.991254</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b7[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05421633</code></pre>
<pre class="r"><code>mean(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.014851</code></pre>
<pre class="r"><code>boot.se(M.attr$b.knowledge.b7[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05219823</code></pre>
<pre class="r"><code>###H11: Satisfaction with RS
mean(M.attr$b.satisfaction.b3[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 4.200803</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b3[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.04807607</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b3[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.315197</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b3[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.0423454</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.146199</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b3[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.06106221</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.332506</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b3[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.04925764</code></pre>
<pre class="r"><code>###H10: Satisfaction with Park Management
mean(M.attr$b.satisfaction.b2[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 4.530938</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b2[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.03969948</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b2[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.4803</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b2[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.04166871</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.48105</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b2[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.05246261</code></pre>
<pre class="r"><code>mean(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 4.471464</code></pre>
<pre class="r"><code>boot.se(M.attr$b.satisfaction.b2[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.04832283</code></pre>
<pre class="r"><code>###H13: Support for Conservation (Importance of Protecting Bwindi)
mean(M.attr$b.conservation.b6[M.attr$treat==1], na.rm=T)</code></pre>
<pre><code>## [1] 3.896414</code></pre>
<pre class="r"><code>boot.se(M.attr$b.conservation.b6[M.attr$treat==1])</code></pre>
<pre><code>## [1] 0.01699529</code></pre>
<pre class="r"><code>mean(M.attr$b.conservation.b6[M.attr$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.889513</code></pre>
<pre class="r"><code>boot.se(M.attr$b.conservation.b6[M.attr$treat==0])</code></pre>
<pre><code>## [1] 0.01608697</code></pre>
<pre class="r"><code>mean(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.892442</code></pre>
<pre class="r"><code>boot.se(M.attr$b.conservation.b6[M.attr$treat==1 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.02182871</code></pre>
<pre class="r"><code>mean(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M$attr==0], na.rm=T)</code></pre>
<pre><code>## [1] 3.89604</code></pre>
<pre class="r"><code>boot.se(M.attr$b.conservation.b6[M.attr$treat==0 &amp; M$attr==0])</code></pre>
<pre><code>## [1] 0.01840154</code></pre>
<pre class="r"><code>######################################################################################
###Figure G1: Reserve power calculation for satisfaction w/ Revenue Sharing
######################################################################################
source('outcome_var_mean_imp_setup.R')
source2('BDD_WD2018_replication.R', start=628, end=629) #sourcing main model results

te.seq &lt;- seq(from=0.01,to=0.5,by=0.01)
power.store &lt;- rep(NA, length(te.seq))

for (i in 1:length(te.seq)){
  nod.h11.power &lt;- felm.ri.power(e.satisfaction.b3 ~ treat + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                                 dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(nod.h11.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.satisfaction.b3&quot;, te=te.seq[i])
  power.store[i] &lt;- nod.h11.power$power
}

##Plotting reverse 
par(mar=c(4.1,4.1,1.1,1.1))
plot(x=te.seq, y=power.store, type=&quot;l&quot;, ylab=&quot;Power&quot;, xlab=&quot;Treatment Effect&quot;)
abline(h=0.8, col=&quot;red&quot;, lty=2)</code></pre>
<p><img 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" width="624" /></p>
<pre class="r"><code>######################################################################################
###Table G1: Minimum Detectable Positive Effect for Each Survey Item
######################################################################################
source('outcome_var_mean_imp_setup.R')

te.seq &lt;- seq(from=0.2, to=0.8, by=0.01)

###H7.2: Know How RS Works Generally
mod.h7.2.ri &lt;- felm.ri(e.knowledge.b8 ~ treat + b.knowledge.b8 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                       dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h7.2.power &lt;- lm.ri.power(e.knowledge.b8 ~ treat + b.knowledge.b8 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h7.2.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.knowledge.b8&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h7.2.power$power
  if (mod.h7.2.power$power &gt;= 0.8) break
} #ate.bar: 0.08173884
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.25</code></pre>
<pre><code>## [1] 0.25</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.knowledge.b8)[length(table(M$e.knowledge.b8))] / sum(table(M$e.knowledge.b8))</code></pre>
<pre><code>##         4 
## 0.5111562</code></pre>
<pre class="r"><code>#std. effect
0.25/sd(M$e.knowledge.b8[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.3562509</code></pre>
<pre class="r"><code>#mde
0.25*(1-0.5111562)</code></pre>
<pre><code>## [1] 0.122211</code></pre>
<pre class="r"><code>###H7.3: How RS Works in my Village
mod.h7.3.ri &lt;- felm.ri(e.knowledge.b9 ~ treat + b.knowledge.b9 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                       dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h7.3.power &lt;- lm.ri.power(e.knowledge.b9 ~ treat + b.knowledge.b9 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h7.3.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.knowledge.b9&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h7.3.power$power
  if (mod.h7.3.power$power &gt;= 0.8) break
} #ate.bar: 0.07572411
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.30</code></pre>
<pre><code>## [1] 0.3</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.knowledge.b9)[length(table(M$e.knowledge.b9))] / sum(table(M$e.knowledge.b9))</code></pre>
<pre><code>##         4 
## 0.6135217</code></pre>
<pre class="r"><code>#std. effect
0.30/sd(M$e.knowledge.b9[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.4679264</code></pre>
<pre class="r"><code>#mde
0.30*(1-0.6135217)</code></pre>
<pre><code>## [1] 0.1159435</code></pre>
<pre class="r"><code>###H9.2: Know Right Person to Contact
mod.h9.2.ri &lt;- lm.ri(e.participate.b12 ~ treat + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                     dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h9.2.power &lt;- lm.ri.power(e.participate.b12 ~ treat + b.participate.b12 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h9.2.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.participate.b12&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h9.2.power$power
  if (mod.h9.2.power$power &gt;= 0.8) break
} #ate.bar: 0.0912536
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.6</code></pre>
<pre><code>## [1] 0.6</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.participate.b12)[length(table(M$e.participate.b12))] / sum(table(M$e.participate.b12))</code></pre>
<pre><code>##         4 
## 0.7741607</code></pre>
<pre class="r"><code>#std. effect
0.60/sd(M$e.participate.b12[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.800684</code></pre>
<pre class="r"><code>#mde
0.60*(1-0.7741607)</code></pre>
<pre><code>## [1] 0.1355036</code></pre>
<pre class="r"><code>###H9.1: Satisfaction with opportunities to communicate with UWA about RS
mod.h9.1.ri &lt;- felm.ri(e.participate.b11 ~ treat + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                       dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h9.1.power &lt;- lm.ri.power(e.participate.b11 ~ treat + b.participate.b11 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h9.1.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.participate.b11&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h9.1.power$power
  if (mod.h9.1.power$power &gt;= 0.8) break
} #ate.bar: 0.1017958
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.25</code></pre>
<pre><code>## [1] 0.25</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.participate.b11)[length(table(M$e.participate.b11))] / sum(table(M$e.participate.b11))</code></pre>
<pre><code>##         5 
## 0.3679435</code></pre>
<pre class="r"><code>#std. effect
0.25/sd(M$e.participate.b11[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.2357983</code></pre>
<pre class="r"><code>#mde
0.25*(1-0.3679435)</code></pre>
<pre><code>## [1] 0.1580141</code></pre>
<pre class="r"><code>###H8.1: Opportunities to Participate in Planning
mod.h8.1.ri &lt;- felm.ri(e.participate.b10 ~ treat + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                       dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h8.1.power &lt;- lm.ri.power(e.participate.b10 ~ treat + b.participate.b10 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h8.1.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.participate.b10&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h8.1.power$power
  if (mod.h8.1.power$power &gt;= 0.8) break
} #ate.bar: 0.1149789
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.57</code></pre>
<pre><code>## [1] 0.57</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.participate.b10)[length(table(M$e.participate.b10))] / sum(table(M$e.participate.b10))</code></pre>
<pre><code>##         4 
## 0.7102616</code></pre>
<pre class="r"><code>#std. effect
0.57/sd(M$e.participate.b10[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.6652535</code></pre>
<pre class="r"><code>#mde
0.57*(1-0.7102616)</code></pre>
<pre><code>## [1] 0.1651509</code></pre>
<pre class="r"><code>###H8.3: Participate in RS meetings in past months
M.ri$e.participate.e4 &lt;- as.numeric(M.ri$e.participate.e4)
mod.h8.3.ri &lt;- felm.ri(e.participate.e4 ~ treat + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                       dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h8.3.power &lt;- lm.ri.power(e.participate.e4 ~ treat + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h8.3.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.participate.e4&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h8.3.power$power
  if (mod.h8.3.power$power &gt;= 0.8) break
} #ate.bar: 0.0509953
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.29</code></pre>
<pre><code>## [1] 0.29</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.participate.e4)[length(table(M$e.participate.e4))] / sum(table(M$e.participate.e4))</code></pre>
<pre><code>##         1 
## 0.7377377</code></pre>
<pre class="r"><code>#mde
0.29*(1-0.7377377)</code></pre>
<pre><code>## [1] 0.07605607</code></pre>
<pre class="r"><code>###H7.1: Satisfaction with RS Info from UWA
mod.h7.ri &lt;- felm.ri(e.knowledge.b7 ~ treat + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                     dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  mod.h7.power &lt;- lm.ri.power(e.knowledge.b7 ~ treat + b.knowledge.b7 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                              dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(mod.h7.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.knowledge.b7&quot;, te=te.seq[i])
  power.store[i] &lt;- mod.h7.power$power
  if (mod.h7.power$power &gt;= 0.8) break
} #ate.bar: 0.09747668
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.23</code></pre>
<pre><code>## [1] 0.23</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.knowledge.b7)[length(table(M$e.knowledge.b7))] / sum(table(M$e.knowledge.b7))</code></pre>
<pre><code>##         5 
## 0.3570712</code></pre>
<pre class="r"><code>#std. effect
0.23/sd(M$e.knowledge.b7[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.2565545</code></pre>
<pre class="r"><code>#mde
0.23*(1-0.3570712)</code></pre>
<pre><code>## [1] 0.1478736</code></pre>
<pre class="r"><code>###H11: Satisfaction with RS
nod.h11.ri &lt;- felm.ri(e.satisfaction.b3 ~ treat + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                      dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  nod.h11.power &lt;- lm.ri.power(e.satisfaction.b3 ~ treat + b.satisfaction.b3 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                               dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(nod.h11.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.satisfaction.b3&quot;, te=te.seq[i])
  power.store[i] &lt;- nod.h11.power$power
  if (nod.h11.power$power &gt;= 0.8) break
} #ate.bar: 0.1525544
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.32</code></pre>
<pre><code>## [1] 0.32</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.satisfaction.b3)[length(table(M$e.satisfaction.b3))] / sum(table(M$e.satisfaction.b3))</code></pre>
<pre><code>##         5 
## 0.2862903</code></pre>
<pre class="r"><code>#std. effect
0.32/sd(M$e.satisfaction.b2[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.3299625</code></pre>
<pre class="r"><code>#mde
0.32*(1-0.2862903)</code></pre>
<pre><code>## [1] 0.2283871</code></pre>
<pre class="r"><code>###H10: Satisfaction with Park Management
nod.h10mgmt.ri &lt;- felm.ri(e.satisfaction.b2 ~ treat + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                          dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  nod.h10mgmt.power &lt;- lm.ri.power(e.satisfaction.b2 ~ treat + b.satisfaction.b2 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                                   dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(nod.h10mgmt.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.satisfaction.b2&quot;, te=te.seq[i])
  power.store[i] &lt;- nod.h10mgmt.power$power
  if (nod.h10mgmt.power$power &gt;= 0.8) break
} #ate.bar: 0.1390786
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.37</code></pre>
<pre><code>## [1] 0.37</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.satisfaction.b2)[length(table(M$e.satisfaction.b2))] / sum(table(M$e.satisfaction.b2))</code></pre>
<pre><code>##         5 
## 0.4407631</code></pre>
<pre class="r"><code>#std. effect
0.37/sd(M$e.satisfaction.b2[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 0.3815192</code></pre>
<pre class="r"><code>#mde
0.37*(1-0.4407631)</code></pre>
<pre><code>## [1] 0.2069177</code></pre>
<pre class="r"><code>###H13: Support for Conservation (Importance of Protecting Bwindi)
nod.h13.ri &lt;- lm.ri(e.conservation.b6 ~ treat + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                    dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)

power.store &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  nod.h13.power &lt;- lm.ri.power(e.conservation.b6 ~ treat + b.conservation.b6 + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                               dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = quantile(sort(nod.h13.ri$ate.samp.dist), probs = 0.95), outcome.var = &quot;e.conservation.b6&quot;, te=te.seq[i])
  power.store[i] &lt;- nod.h13.power$power
  if (nod.h13.power$power &gt;= 0.8) break
} #ate.bar: 0.03871926
min(te.seq[power.store&gt;=0.8 &amp; !is.na(power.store)]) #0.60</code></pre>
<pre><code>## [1] 0.6</code></pre>
<pre class="r"><code>#prop. ceiling obs
table(M$e.conservation.b6)[length(table(M$e.conservation.b6))] / sum(table(M$e.conservation.b6))</code></pre>
<pre><code>##         4 
## 0.9078156</code></pre>
<pre class="r"><code>#std. effect
0.60/sd(M$e.conservation.b6[M$treat==0], na.rm=T)</code></pre>
<pre><code>## [1] 1.744885</code></pre>
<pre class="r"><code>#mde
0.60*(1-0.9078156)</code></pre>
<pre><code>## [1] 0.05531064</code></pre>
<pre class="r"><code>#for paragraph
table(M$e.conservation.b6)</code></pre>
<pre><code>## 
##   2   3   4 
##   9  83 906</code></pre>
<pre class="r"><code>######################################################################################
###Table G2: Treatment Effects on Indices of Knowledge, Participation, and Satisfaction
######################################################################################
source('outcome_var_mean_imp_setup.R')

M$e.participate.e4 &lt;- as.numeric(M$e.participate.e4)

M$e.knowledge &lt;- M$e.knowledge.b8*1.25 + M$e.knowledge.b9*1.25 + M$e.participate.b12*1.25
M$e.participate &lt;- M$e.participate.b10*1.25 + M$e.participate.e4*5 + M$e.participate.b11
M$e.satisfaction &lt;- M$e.knowledge.b7 + M$e.satisfaction.b2 + M$e.satisfaction.b3 + M$e.conservation.b6*1.25
#Note: NA values on single items will result in NA values on index automatically

#To find out proportion at ceiling
table(M$e.knowledge)</code></pre>
<pre><code>## 
##  3.75     5  6.25   7.5  8.75    10 11.25  12.5 13.75    15 
##     6     3     8    16    30    49    86   179   211   380</code></pre>
<pre class="r"><code>table(M$e.participate)</code></pre>
<pre><code>## 
##  2.25  3.25  4.25   4.5  4.75  5.25   5.5  5.75     6  6.25   6.5  6.75 
##     3    17     5    10     3    12     4    12     6    12     6    11 
##     7  7.25   7.5  7.75     8  8.25   8.5  8.75     9  9.25   9.5  9.75 
##    20     2     8    24     9     5     3     6    48     2    11     2 
##    10 10.25  10.5 10.75    11 11.25  11.5 11.75    12  12.5 12.75    13 
##    37     3     5    14     2    13    13     4    43    11    47    38 
## 13.75    14    15 
##    17   238   261</code></pre>
<pre class="r"><code>table(M$e.satisfaction)</code></pre>
<pre><code>## 
##   5.5   6.5  8.75     9   9.5  9.75    10 10.75    11  11.5 11.75    12 
##     1     1     1     1     1     7     3     6    11     1    12     9 
##  12.5 12.75    13  13.5 13.75    14  14.5 14.75    15  15.5 15.75    16 
##     1     7    36     1    19    23     1     4    84     1    17    83 
## 16.75    17 17.75    18    19    20 
##     6   198     2   161   162   128</code></pre>
<pre class="r"><code>M$b.knowledge &lt;- M$b.knowledge.b8*1.25 + M$b.knowledge.b9*1.25 + M$b.participate.b12*1.25
M$b.participate &lt;- M$b.participate.b10*1.25 + M$b.participate.b11
M$b.satisfaction &lt;- M$b.knowledge.b7 + M$b.satisfaction.b2 + M$b.satisfaction.b3 + M$b.conservation.b6*1.25

M.ri &lt;- merge(M, sbj_RI, by=&quot;Subject_ID_number&quot;, all.x=TRUE)

###RI inference on the indices, generating ate.bar from felm.ri

knowledge.ri &lt;- felm.ri(e.knowledge ~ treat + b.knowledge + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                        dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)
#ate: -0.005619
#se: 0.1484776
#p.two.way: 0.9709
#p.one.way.greater: 0.5247
#p.one.way.lesser: 0.4753
#quantile(sort(knowledge.ri$ate.samp.dist), probs = 0.95) #0.2481223

participate.ri &lt;- felm.ri(e.participate ~ treat + b.participate + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                          dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)
#ate: -0.3809
#se: 0.2520316
#p.two.way: 0.1325
#p.one.way.greater: 0.9356
#p.one.way.lesser: 0.0644
#quantile(sort(participate.ri$ate.samp.dist), probs = 0.95) #0.415441

satisfaction.ri &lt;- felm.ri(e.satisfaction ~ treat + b.satisfaction + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village | subcounty.blocking | 0 | subcounty.blocking,
                           dta=M.ri, treat.var = &quot;treat&quot;, sims=10000)
#ate: 0.012490
#se: 0.2210442
#p.two.way: 0.9544
#p.one.way.greater: 0.4812
#p.one.way.lesser: 0.5188
#quantile(sort(satisfaction.ri$ate.samp.dist), probs = 0.95) #0.3641675


######################################################################################
###Figure G2: Power on Family-Based Indices
#Note: must run code for Table G2
######################################################################################
#Note: lm.ri.power is faster (no error clustering), but gives same results

te.seq &lt;- seq(from=0.05,to=0.95,by=0.05)

power.store.k &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  k.power &lt;- lm.ri.power(e.knowledge ~ treat + b.knowledge + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.2481223, outcome.var = &quot;e.knowledge&quot;, te=te.seq[i])
  power.store.k[i] &lt;- k.power$power
}

power.store.p &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  p.power &lt;- lm.ri.power(e.participate ~ treat + b.participate + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.415441, outcome.var = &quot;e.participate&quot;, te=te.seq[i])
  power.store.p[i] &lt;- p.power$power
}

power.store.s &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  s.power &lt;- lm.ri.power(e.satisfaction ~ treat + b.satisfaction + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.3641675, outcome.var = &quot;e.satisfaction&quot;, te=te.seq[i])
  power.store.s[i] &lt;- s.power$power
}

min(te.seq[power.store.k&gt;=0.8]) #0.65</code></pre>
<pre><code>## [1] 0.65</code></pre>
<pre class="r"><code>min(te.seq[power.store.p&gt;=0.8]) #0.85</code></pre>
<pre><code>## [1] 0.85</code></pre>
<pre class="r"><code>min(te.seq[power.store.s&gt;=0.8]) #0.65</code></pre>
<pre><code>## [1] 0.65</code></pre>
<pre class="r"><code>##Plotting reverse 

#pdf(file=&quot;FigH2_IndexPower_170907.pdf&quot;, width=6.5, height=5)
par(mar=c(3.6,3.1,1.1,1.1))
par(mgp=c(2,1,0))
par(oma=c(0,0,0,0))
par(mfrow=c(3,1))

plot(x=te.seq, y=power.store.k, type=&quot;l&quot;, ylab=&quot;Power&quot;, xlab=&quot;Treatment Effect&quot;, main=&quot;Knowledge&quot;)
abline(h=0.8, col=&quot;red&quot;, lty=2)

plot(x=te.seq, y=power.store.p, type=&quot;l&quot;, ylab=&quot;Power&quot;, xlab=&quot;Treatment Effect&quot;, main=&quot;Participation&quot;)
abline(h=0.8, col=&quot;red&quot;, lty=2)

plot(x=te.seq, y=power.store.s, type=&quot;l&quot;, ylab=&quot;Power&quot;, xlab=&quot;Treatment Effect&quot;, main=&quot;Satisfaction&quot;)
abline(h=0.8, col=&quot;red&quot;, lty=2)</code></pre>
<p><img 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" width="624" /></p>
<pre class="r"><code>#dev.off()

##Precise power calculations for MDE

te.seq &lt;- seq(from=0.6,to=0.65,by=0.001)
power.store.k &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  k.power &lt;- lm.ri.power(e.knowledge ~ treat + b.knowledge + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.2481223, outcome.var = &quot;e.knowledge&quot;, te=te.seq[i])
  power.store.k[i] &lt;- k.power$power
}

te.seq &lt;- seq(from=0.8,to=0.85,by=0.001)
power.store.p &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  p.power &lt;- lm.ri.power(e.participate ~ treat + b.participate + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.415441, outcome.var = &quot;e.participate&quot;, te=te.seq[i])
  power.store.p[i] &lt;- p.power$power
}

te.seq &lt;- seq(from=0.6,to=0.65,by=0.001)
power.store.s &lt;- rep(NA, length(te.seq))
for (i in 1:length(te.seq)){
  s.power &lt;- lm.ri.power(e.satisfaction ~ treat + b.satisfaction + E_subject_s_gender + E_Subject_s_age + E_Subject_s_income + E_Can_you_read_001 + subjects.per.village + subcounty.blocking,
                         dta=M.ri, treat.var = &quot;treat&quot;, sims=10000, ate.bar = 0.3641675, outcome.var = &quot;e.satisfaction&quot;, te=te.seq[i])
  power.store.s[i] &lt;- s.power$power
}

min(te.seq[power.store.k&gt;=0.8]) #0.603</code></pre>
<pre><code>## [1] 0.602</code></pre>
<pre class="r"><code>min(te.seq[power.store.s&gt;=0.8]) #0.631</code></pre>
<pre><code>## [1] 0.63</code></pre>
<pre class="r"><code>te.seq &lt;- seq(from=0.8,to=0.85,by=0.001)
min(te.seq[power.store.p&gt;=0.8]) #0.845</code></pre>
<pre><code>## [1] 0.844</code></pre>
<pre class="r"><code>mean(M$e.knowledge, na.rm=T)</code></pre>
<pre><code>## [1] 13.18827</code></pre>
<pre class="r"><code>######################################################################################
###Table H1: Modeling number of subjects per village as a function of village-level characteristics
######################################################################################
source('outcome_var_setup.R')

pull.mean &lt;- function(source, var, id){
  id.list &lt;- sort(unique(source[,id]))
  store &lt;- rep(NA, length(unique(source[,id])))
  for (i in 1:length(unique(source[,id]))){
    store[i] &lt;- mean(source[source[,id]==id.list[i],var], na.rm=TRUE)
  }
  return(store)
}

village.dta &lt;- data.frame(sort(unique(M$Updated.Village)))
village.dta$subjects.per.village &lt;- pull.mean(source=M, var=&quot;subjects.per.village&quot;, id=&quot;Updated.Village&quot;)
village.dta$b.knowledge &lt;- pull.mean(source=M, var=&quot;b.knowledge&quot;, id=&quot;Updated.Village&quot;)
village.dta$b.participate &lt;- pull.mean(source=M, var=&quot;b.participate&quot;, id=&quot;Updated.Village&quot;)
village.dta$b.satisfaction &lt;- pull.mean(source=M, var=&quot;b.satisfaction&quot;, id=&quot;Updated.Village&quot;)
village.dta$age &lt;- pull.mean(source=M, var=&quot;E_Subject_s_age&quot;, id=&quot;Updated.Village&quot;)

M$female &lt;- ifelse(M$E_subject_s_gender==&quot;female&quot;,1,0)
village.dta$female &lt;- pull.mean(source=M, var=&quot;female&quot;, id=&quot;Updated.Village&quot;)

M$literate &lt;- ifelse(M$E_Can_you_read_001==&quot;yes__i_can_rea&quot;,1,0) #total literacy
village.dta$literate &lt;- pull.mean(source=M, var=&quot;literate&quot;, id=&quot;Updated.Village&quot;)

M$poverty &lt;- ifelse(M$E_Subject_s_income==&quot;20_000_shillin&quot; | M$E_Subject_s_income==&quot;20_000___100_0&quot;, 1, 0)
M$poverty[M$E_Subject_s_income==&quot;refused_to_ans&quot;] &lt;- NA
village.dta$poverty &lt;- pull.mean(source=M, var=&quot;poverty&quot;, id=&quot;Updated.Village&quot;)

mod &lt;- lm(subjects.per.village ~ b.knowledge + b.participate + b.satisfaction + age + female + literate + poverty, data=village.dta)
summary(mod)</code></pre>
<pre><code>## 
## Call:
## lm(formula = subjects.per.village ~ b.knowledge + b.participate + 
##     b.satisfaction + age + female + literate + poverty, data = village.dta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.325  -5.772  -2.164   4.087  32.954 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)  
## (Intercept)    30.56780   19.69723   1.552   0.1245  
## b.knowledge    -2.15934    1.12424  -1.921   0.0582 .
## b.participate  -0.46095    1.40144  -0.329   0.7431  
## b.satisfaction  0.72196    1.16295   0.621   0.5364  
## age             0.08499    0.22563   0.377   0.7074  
## female          3.23601    5.89716   0.549   0.5847  
## literate        2.41773    5.36260   0.451   0.6533  
## poverty         4.16890    5.60425   0.744   0.4590  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.69 on 83 degrees of freedom
## Multiple R-squared:  0.08268,    Adjusted R-squared:  0.005319 
## F-statistic: 1.069 on 7 and 83 DF,  p-value: 0.3909</code></pre>
<pre class="r"><code>######################################################################################
###Table H2: Modeling number of subjects per village as a function of village-level characteristics
######################################################################################
source('outcome_var_mean_imp_setup.R')

###H7.2: Know How RS Works Generally
mod.h7.2.d &lt;- lm(e.knowledge.b8 ~ treat*subjects.per.village + b.knowledge.b8 + subcounty.blocking, data=M)
summary(mod.h7.2.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.knowledge.b8 ~ treat * subjects.per.village + 
##     b.knowledge.b8 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5295 -0.4563  0.3376  0.5558  1.3156 
## 
## Coefficients:
##                                          Estimate Std. Error t value
## (Intercept)                             2.5411772  0.1468514  17.304
## treat                                  -0.0983778  0.1120084  -0.878
## subjects.per.village                   -0.0002947  0.0033689  -0.087
## b.knowledge.b8                          0.1852653  0.0316870   5.847
## subcounty.blockingKanungu Town Council  0.4524541  0.1361478   3.323
## subcounty.blockingKayonza               0.2054526  0.0870491   2.360
## subcounty.blockingKinaaba               0.2378788  0.1353726   1.757
## subcounty.blockingKirima                0.2031059  0.0849667   2.390
## subcounty.blockingKirundo               0.1706877  0.1267441   1.347
## subcounty.blockingMpungu                0.3092164  0.0794060   3.894
## subcounty.blockingNyabwishenya          0.2956608  0.1320161   2.240
## subcounty.blockingRuhija                0.2584794  0.0723213   3.574
## subcounty.blockingSingles               0.2303881  0.1419866   1.623
## treat:subjects.per.village              0.0046278  0.0045069   1.027
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                  0.379994    
## subjects.per.village                   0.930303    
## b.knowledge.b8                         6.84e-09 ***
## subcounty.blockingKanungu Town Council 0.000923 ***
## subcounty.blockingKayonza              0.018462 *  
## subcounty.blockingKinaaba              0.079196 .  
## subcounty.blockingKirima               0.017019 *  
## subcounty.blockingKirundo              0.178387    
## subcounty.blockingMpungu               0.000105 ***
## subcounty.blockingNyabwishenya         0.025343 *  
## subcounty.blockingRuhija               0.000369 ***
## subcounty.blockingSingles              0.104998    
## treat:subjects.per.village             0.304752    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7225 on 972 degrees of freedom
##   (686 observations deleted due to missingness)
## Multiple R-squared:  0.06145,    Adjusted R-squared:  0.0489 
## F-statistic: 4.896 on 13 and 972 DF,  p-value: 2.305e-08</code></pre>
<pre class="r"><code>###H7.3: How RS Works in my Village
mod.h7.3.d &lt;- lm(e.knowledge.b9 ~ treat*subjects.per.village + b.knowledge.b9 + subcounty.blocking, data=M)
summary(mod.h7.3.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.knowledge.b9 ~ treat * subjects.per.village + 
##     b.knowledge.b9 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4795 -0.4972  0.3424  0.4852  1.0459 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             2.933820   0.145458  20.170
## treat                                  -0.160336   0.103780  -1.545
## subjects.per.village                   -0.004055   0.003130  -1.295
## b.knowledge.b9                          0.165964   0.031301   5.302
## subcounty.blockingKanungu Town Council  0.238406   0.124705   1.912
## subcounty.blockingKayonza               0.051253   0.080487   0.637
## subcounty.blockingKinaaba               0.332623   0.125847   2.643
## subcounty.blockingKirima                0.137014   0.078260   1.751
## subcounty.blockingKirundo               0.120742   0.117571   1.027
## subcounty.blockingMpungu                0.172345   0.073093   2.358
## subcounty.blockingNyabwishenya          0.295375   0.122475   2.412
## subcounty.blockingRuhija                0.129868   0.066559   1.951
## subcounty.blockingSingles               0.146691   0.132027   1.111
## treat:subjects.per.village              0.005386   0.004169   1.292
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                   0.12268    
## subjects.per.village                    0.19549    
## b.knowledge.b9                         1.42e-07 ***
## subcounty.blockingKanungu Town Council  0.05620 .  
## subcounty.blockingKayonza               0.52442    
## subcounty.blockingKinaaba               0.00835 ** 
## subcounty.blockingKirima                0.08030 .  
## subcounty.blockingKirundo               0.30469    
## subcounty.blockingMpungu                0.01857 *  
## subcounty.blockingNyabwishenya          0.01606 *  
## subcounty.blockingRuhija                0.05132 .  
## subcounty.blockingSingles               0.26681    
## treat:subjects.per.village              0.19672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6706 on 977 degrees of freedom
##   (681 observations deleted due to missingness)
## Multiple R-squared:  0.04768,    Adjusted R-squared:  0.03501 
## F-statistic: 3.763 on 13 and 977 DF,  p-value: 6.544e-06</code></pre>
<pre class="r"><code>###H9.2: Know Right Person to Contact
mod.h9.2.d &lt;- lm(e.participate.b12 ~ treat*subjects.per.village + b.participate.b12 + subcounty.blocking, data=M)
summary(mod.h9.2.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.participate.b12 ~ treat * subjects.per.village + 
##     b.participate.b12 + subcounty.blocking, data = M)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.84275  0.07046  0.29668  0.40130  0.82937 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             3.271894   0.153799  21.274
## treat                                  -0.046262   0.118884  -0.389
## subjects.per.village                   -0.006186   0.003590  -1.723
## b.participate.b12                       0.135918   0.031044   4.378
## subcounty.blockingKanungu Town Council  0.228356   0.142507   1.602
## subcounty.blockingKayonza              -0.115144   0.092098  -1.250
## subcounty.blockingKinaaba               0.188271   0.143405   1.313
## subcounty.blockingKirima               -0.023322   0.089598  -0.260
## subcounty.blockingKirundo               0.138530   0.134469   1.030
## subcounty.blockingMpungu                0.010256   0.086630   0.118
## subcounty.blockingNyabwishenya          0.288846   0.139922   2.064
## subcounty.blockingRuhija                0.070250   0.076322   0.920
## subcounty.blockingSingles              -0.089275   0.150523  -0.593
## treat:subjects.per.village              0.001929   0.004791   0.403
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                    0.6973    
## subjects.per.village                     0.0852 .  
## b.participate.b12                      1.33e-05 ***
## subcounty.blockingKanungu Town Council   0.1094    
## subcounty.blockingKayonza                0.2115    
## subcounty.blockingKinaaba                0.1895    
## subcounty.blockingKirima                 0.7947    
## subcounty.blockingKirundo                0.3032    
## subcounty.blockingMpungu                 0.9058    
## subcounty.blockingNyabwishenya           0.0393 *  
## subcounty.blockingRuhija                 0.3576    
## subcounty.blockingSingles                0.5533    
## treat:subjects.per.village               0.6873    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7671 on 969 degrees of freedom
##   (689 observations deleted due to missingness)
## Multiple R-squared:  0.04269,    Adjusted R-squared:  0.02984 
## F-statistic: 3.324 on 13 and 969 DF,  p-value: 5.398e-05</code></pre>
<pre class="r"><code>###H9.1: Satisfaction with opportunities to communicate with UWA about RS
mod.h9.1.d &lt;- lm(e.participate.b11 ~ treat*subjects.per.village + b.participate.b11 + subcounty.blocking, data=M)
summary(mod.h9.1.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.participate.b11 ~ treat * subjects.per.village + 
##     b.participate.b11 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1768 -0.3546  0.1351  0.9680  1.4210 
## 
## Coefficients:
##                                          Estimate Std. Error t value
## (Intercept)                             3.5104253  0.1947971  18.021
## treat                                  -0.0444102  0.1667403  -0.266
## subjects.per.village                    0.0004564  0.0050043   0.091
## b.participate.b11                       0.0781901  0.0335993   2.327
## subcounty.blockingKanungu Town Council  0.4843947  0.2006456   2.414
## subcounty.blockingKayonza               0.1957361  0.1297729   1.508
## subcounty.blockingKinaaba               0.0284963  0.2007133   0.142
## subcounty.blockingKirima                0.2125836  0.1262306   1.684
## subcounty.blockingKirundo               0.3062758  0.1909792   1.604
## subcounty.blockingMpungu                0.1301388  0.1189432   1.094
## subcounty.blockingNyabwishenya          0.7644071  0.1970782   3.879
## subcounty.blockingRuhija                0.0757803  0.1070547   0.708
## subcounty.blockingSingles               0.1711196  0.2122810   0.806
## treat:subjects.per.village              0.0003721  0.0067039   0.056
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                  0.790031    
## subjects.per.village                   0.927349    
## b.participate.b11                      0.020162 *  
## subcounty.blockingKanungu Town Council 0.015953 *  
## subcounty.blockingKayonza              0.131801    
## subcounty.blockingKinaaba              0.887129    
## subcounty.blockingKirima               0.092484 .  
## subcounty.blockingKirundo              0.109100    
## subcounty.blockingMpungu               0.274169    
## subcounty.blockingNyabwishenya         0.000112 ***
## subcounty.blockingRuhija               0.479197    
## subcounty.blockingSingles              0.420382    
## treat:subjects.per.village             0.955743    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.08 on 978 degrees of freedom
##   (680 observations deleted due to missingness)
## Multiple R-squared:  0.02714,    Adjusted R-squared:  0.01421 
## F-statistic: 2.099 on 13 and 978 DF,  p-value: 0.0122</code></pre>
<pre class="r"><code>###H8.1: Opportunities to Participate in Planning
mod.h8.1.d &lt;- lm(e.participate.b10 ~ treat*subjects.per.village + b.participate.b10 + subcounty.blocking, data=M)
summary(mod.h8.1.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.participate.b10 ~ treat * subjects.per.village + 
##     b.participate.b10 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7832 -0.3870  0.4203  0.5356  0.9864 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             3.230133   0.157250  20.541
## treat                                  -0.189990   0.141484  -1.343
## subjects.per.village                   -0.004125   0.004250  -0.971
## b.participate.b10                       0.120687   0.031632   3.815
## subcounty.blockingKanungu Town Council  0.136332   0.170254   0.801
## subcounty.blockingKayonza              -0.080183   0.110692  -0.724
## subcounty.blockingKinaaba               0.143778   0.170720   0.842
## subcounty.blockingKirima                0.008645   0.107097   0.081
## subcounty.blockingKirundo              -0.166206   0.160740  -1.034
## subcounty.blockingMpungu                0.079235   0.099715   0.795
## subcounty.blockingNyabwishenya         -0.105245   0.167206  -0.629
## subcounty.blockingRuhija                0.066603   0.091214   0.730
## subcounty.blockingSingles               0.007404   0.180292   0.041
## treat:subjects.per.village              0.002891   0.005699   0.507
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                  0.179634    
## subjects.per.village                   0.331923    
## b.participate.b10                      0.000144 ***
## subcounty.blockingKanungu Town Council 0.423467    
## subcounty.blockingKayonza              0.469003    
## subcounty.blockingKinaaba              0.399889    
## subcounty.blockingKirima               0.935678    
## subcounty.blockingKirundo              0.301387    
## subcounty.blockingMpungu               0.427029    
## subcounty.blockingNyabwishenya         0.529212    
## subcounty.blockingRuhija               0.465455    
## subcounty.blockingSingles              0.967253    
## treat:subjects.per.village             0.612106    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9164 on 980 degrees of freedom
##   (678 observations deleted due to missingness)
## Multiple R-squared:  0.02532,    Adjusted R-squared:  0.01239 
## F-statistic: 1.958 on 13 and 980 DF,  p-value: 0.02126</code></pre>
<pre class="r"><code>###H8.3: Participate in RS meetings in past months
mod.h8.3.d &lt;- lm(e.participate.e4 ~ treat*subjects.per.village + subcounty.blocking, data=M)
summary(mod.h8.3.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.participate.e4 ~ treat * subjects.per.village + 
##     subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9269 -0.6117  0.2259  0.2981  0.3946 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             0.858494   0.057393  14.958
## treat                                  -0.194190   0.067180  -2.891
## subjects.per.village                   -0.007030   0.002015  -3.489
## subcounty.blockingKanungu Town Council  0.099598   0.081009   1.229
## subcounty.blockingKayonza               0.145730   0.052178   2.793
## subcounty.blockingKinaaba               0.223631   0.080999   2.761
## subcounty.blockingKirima                0.028441   0.050774   0.560
## subcounty.blockingKirundo              -0.039171   0.076340  -0.513
## subcounty.blockingMpungu                0.034417   0.047411   0.726
## subcounty.blockingNyabwishenya         -0.019058   0.079546  -0.240
## subcounty.blockingRuhija                0.065958   0.042952   1.536
## subcounty.blockingSingles               0.146580   0.085546   1.713
## treat:subjects.per.village              0.007119   0.002702   2.635
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                  0.003929 ** 
## subjects.per.village                   0.000507 ***
## subcounty.blockingKanungu Town Council 0.219193    
## subcounty.blockingKayonza              0.005324 ** 
## subcounty.blockingKinaaba              0.005871 ** 
## subcounty.blockingKirima               0.575506    
## subcounty.blockingKirundo              0.607985    
## subcounty.blockingMpungu               0.468051    
## subcounty.blockingNyabwishenya         0.810704    
## subcounty.blockingRuhija               0.124952    
## subcounty.blockingSingles              0.086940 .  
## treat:subjects.per.village             0.008540 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4362 on 986 degrees of freedom
##   (673 observations deleted due to missingness)
## Multiple R-squared:  0.02952,    Adjusted R-squared:  0.01771 
## F-statistic:   2.5 on 12 and 986 DF,  p-value: 0.0031</code></pre>
<pre class="r"><code>###H7.1: Satisfaction with RS Info from UWA
mod.h7.d &lt;- lm(e.knowledge.b7 ~ treat*subjects.per.village + b.knowledge.b7 + subcounty.blocking, data=M)
summary(mod.h7.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.knowledge.b7 ~ treat * subjects.per.village + 
##     b.knowledge.b7 + subcounty.blocking, data = M)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.14513 -0.20579 -0.03607  0.81469  1.38801 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             3.820453   0.172564  22.139
## treat                                  -0.222038   0.141414  -1.570
## subjects.per.village                   -0.007750   0.004239  -1.828
## b.knowledge.b7                          0.107613   0.031150   3.455
## subcounty.blockingKanungu Town Council  0.262672   0.170407   1.541
## subcounty.blockingKayonza              -0.110556   0.109871  -1.006
## subcounty.blockingKinaaba               0.124848   0.170539   0.732
## subcounty.blockingKirima                0.111599   0.106901   1.044
## subcounty.blockingKirundo               0.210238   0.160557   1.309
## subcounty.blockingMpungu                0.001946   0.099912   0.019
## subcounty.blockingNyabwishenya         -0.013069   0.167323  -0.078
## subcounty.blockingRuhija               -0.067589   0.090509  -0.747
## subcounty.blockingSingles               0.092742   0.180088   0.515
## treat:subjects.per.village              0.010110   0.005687   1.778
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                  0.116708    
## subjects.per.village                   0.067785 .  
## b.knowledge.b7                         0.000574 ***
## subcounty.blockingKanungu Town Council 0.123531    
## subcounty.blockingKayonza              0.314550    
## subcounty.blockingKinaaba              0.464295    
## subcounty.blockingKirima               0.296769    
## subcounty.blockingKirundo              0.190695    
## subcounty.blockingMpungu               0.984463    
## subcounty.blockingNyabwishenya         0.937762    
## subcounty.blockingRuhija               0.455388    
## subcounty.blockingSingles              0.606682    
## treat:subjects.per.village             0.075733 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9171 on 983 degrees of freedom
##   (675 observations deleted due to missingness)
## Multiple R-squared:  0.0257, Adjusted R-squared:  0.01282 
## F-statistic: 1.995 on 13 and 983 DF,  p-value: 0.01842</code></pre>
<pre class="r"><code>###H11: Satisfaction with RS
nod.h11.d &lt;- lm(e.satisfaction.b3 ~ treat*subjects.per.village + b.satisfaction.b3 + subcounty.blocking, data=M)
summary(nod.h11.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.satisfaction.b3 ~ treat * subjects.per.village + 
##     b.satisfaction.b3 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1113 -0.6491  0.1888  0.8880  2.0122 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             2.991230   0.205692  14.542
## treat                                  -0.145495   0.166722  -0.873
## subjects.per.village                   -0.010197   0.005036  -2.025
## b.satisfaction.b3                       0.213270   0.037677   5.660
## subcounty.blockingKanungu Town Council -0.031233   0.200532  -0.156
## subcounty.blockingKayonza               0.140690   0.130719   1.076
## subcounty.blockingKinaaba               0.556984   0.202232   2.754
## subcounty.blockingKirima                0.266400   0.126069   2.113
## subcounty.blockingKirundo               0.402097   0.189033   2.127
## subcounty.blockingMpungu               -0.022083   0.117328  -0.188
## subcounty.blockingNyabwishenya          0.292745   0.196814   1.487
## subcounty.blockingRuhija                0.146194   0.106808   1.369
## subcounty.blockingSingles               0.399565   0.214718   1.861
## treat:subjects.per.village              0.005259   0.006728   0.782
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                   0.38305    
## subjects.per.village                    0.04317 *  
## b.satisfaction.b3                      1.98e-08 ***
## subcounty.blockingKanungu Town Council  0.87626    
## subcounty.blockingKayonza               0.28207    
## subcounty.blockingKinaaba               0.00599 ** 
## subcounty.blockingKirima                0.03484 *  
## subcounty.blockingKirundo               0.03366 *  
## subcounty.blockingMpungu                0.85075    
## subcounty.blockingNyabwishenya          0.13723    
## subcounty.blockingRuhija                0.17139    
## subcounty.blockingSingles               0.06306 .  
## treat:subjects.per.village              0.43459    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.079 on 978 degrees of freedom
##   (680 observations deleted due to missingness)
## Multiple R-squared:  0.05929,    Adjusted R-squared:  0.04678 
## F-statistic: 4.741 on 13 and 978 DF,  p-value: 5.029e-08</code></pre>
<pre class="r"><code>###H10: Satisfaction with Park Management
nod.h10mgmt.d &lt;- lm(e.satisfaction.b2 ~ treat*subjects.per.village + b.satisfaction.b2 + subcounty.blocking, data=M)
summary(nod.h10mgmt.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.satisfaction.b2 ~ treat * subjects.per.village + 
##     b.satisfaction.b2 + subcounty.blocking, data = M)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3410 -0.2566 -0.0437  0.7725  1.6483 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             3.495273   0.205665  16.995
## treat                                  -0.268412   0.152043  -1.765
## subjects.per.village                   -0.007320   0.004557  -1.606
## b.satisfaction.b2                       0.177473   0.036544   4.856
## subcounty.blockingKanungu Town Council  0.057726   0.183575   0.314
## subcounty.blockingKayonza               0.057827   0.118054   0.490
## subcounty.blockingKinaaba               0.269438   0.183219   1.471
## subcounty.blockingKirima                0.051699   0.114831   0.450
## subcounty.blockingKirundo               0.285894   0.172640   1.656
## subcounty.blockingMpungu                0.020880   0.107675   0.194
## subcounty.blockingNyabwishenya         -0.039937   0.179882  -0.222
## subcounty.blockingRuhija               -0.042910   0.097285  -0.441
## subcounty.blockingSingles               0.053491   0.193848   0.276
## treat:subjects.per.village              0.011991   0.006119   1.959
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                    0.0778 .  
## subjects.per.village                     0.1086    
## b.satisfaction.b2                      1.39e-06 ***
## subcounty.blockingKanungu Town Council   0.7532    
## subcounty.blockingKayonza                0.6244    
## subcounty.blockingKinaaba                0.1417    
## subcounty.blockingKirima                 0.6527    
## subcounty.blockingKirundo                0.0980 .  
## subcounty.blockingMpungu                 0.8463    
## subcounty.blockingNyabwishenya           0.8243    
## subcounty.blockingRuhija                 0.6593    
## subcounty.blockingSingles                0.7827    
## treat:subjects.per.village               0.0503 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9863 on 982 degrees of freedom
##   (676 observations deleted due to missingness)
## Multiple R-squared:  0.032,  Adjusted R-squared:  0.01918 
## F-statistic: 2.497 on 13 and 982 DF,  p-value: 0.00231</code></pre>
<pre class="r"><code>###H13: Support for Conservation (Importance of Protecting Bwindi)
nod.h13.d &lt;- lm(e.conservation.b6 ~ treat*subjects.per.village + b.conservation.b6 + subcounty.blocking, data=M)
summary(nod.h13.d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = e.conservation.b6 ~ treat * subjects.per.village + 
##     b.conservation.b6 + subcounty.blocking, data = M)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.93137  0.05504  0.08102  0.11838  0.48476 
## 
## Coefficients:
##                                         Estimate Std. Error t value
## (Intercept)                             3.373748   0.129087  26.135
## treat                                  -0.070650   0.050396  -1.402
## subjects.per.village                   -0.001941   0.001515  -1.281
## b.conservation.b6                       0.129800   0.030721   4.225
## subcounty.blockingKanungu Town Council  0.082068   0.060718   1.352
## subcounty.blockingKayonza               0.067551   0.039095   1.728
## subcounty.blockingKinaaba               0.160077   0.060879   2.629
## subcounty.blockingKirima                0.066039   0.038043   1.736
## subcounty.blockingKirundo               0.025867   0.057232   0.452
## subcounty.blockingMpungu                0.030194   0.035524   0.850
## subcounty.blockingNyabwishenya          0.088017   0.059602   1.477
## subcounty.blockingRuhija                0.083049   0.032222   2.577
## subcounty.blockingSingles               0.095413   0.064103   1.488
## treat:subjects.per.village              0.003811   0.002028   1.879
##                                        Pr(&gt;|t|)    
## (Intercept)                             &lt; 2e-16 ***
## treat                                   0.16126    
## subjects.per.village                    0.20051    
## b.conservation.b6                      2.61e-05 ***
## subcounty.blockingKanungu Town Council  0.17681    
## subcounty.blockingKayonza               0.08433 .  
## subcounty.blockingKinaaba               0.00869 ** 
## subcounty.blockingKirima                0.08290 .  
## subcounty.blockingKirundo               0.65140    
## subcounty.blockingMpungu                0.39556    
## subcounty.blockingNyabwishenya          0.14006    
## subcounty.blockingRuhija                0.01010 *  
## subcounty.blockingSingles               0.13696    
## treat:subjects.per.village              0.06053 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3268 on 984 degrees of freedom
##   (674 observations deleted due to missingness)
## Multiple R-squared:  0.0339, Adjusted R-squared:  0.02113 
## F-statistic: 2.656 on 13 and 984 DF,  p-value: 0.001153</code></pre>




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